As the demand for multi-dimensional ground and air information acquisition increases on modern battlefields, ground-air cooperation has become a crucial method to enhance operational efficiency and decision-making. In the complex and changeable battlefield environment, unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) can leverage their strengths to maximize exploration efficiency. The multi-level collaborative path planning method for UGV-UAV systems (MLCPP-UUs) is proposed in this paper, comprising three layers: single-UAV, multi-UGV and interactive planning. To optimize the total cost of the model, multiple strategies are used to improve the global exploration and local search of the walrus optimizer (WO). In search-enhanced walrus optimizer (SEWO), a dynamic step size adjustment is introduced during migration based on terrain steepness to avoid blind random search and improve search space coverage. In the later iteration, a nonlinear decreasing search factor is used to accelerate the convergence speed. For high-quality solutions, the simplex method is used to complete the “secondary exploitation" by efficiently searching the neighborhood of the solution space. To evaluate the performance of SEWO, three reference terrains from real digital elevation models (DEM) are generated with obstacle scenarios and interaction modes. The results show that the proposed algorithm can plan the collaborative paths satisfying the constraints efficiently, proving its effectiveness in the ground-air cooperative planning problem.

DNA strand displacement (DSD) is an experimental technology based on DNA molecules, which has a wide range of prospects for application in the fields of molecular biology, nanotechnology and biomedicine. In this paper, classical conditioned reflexes and operant conditioning based on DSD are researched and applied to path planning of unmanned aerial vehicles (UAVs). Firstly, classical conditioned reflexes and four kinds of operant conditioning are studied. Secondly, the process of using the integral system principle to train dogs is modeled based on DSD. Thirdly, the combination of classical conditioned reflexes and operant conditioning is investigated. Finally, the application of conditioned reflexes in path planning of UAVs is proposed. All experiments in this paper are validated using Visual DSD software. The combination of DSD technology and conditioned reflexes provides a research idea for intelligent IoT systems.

Temporal memory is an important component of the memory system, which has the ability to remember a series of events in chronological order. Although simple temporal memory and recall processes are implemented by some memristor based neural networks. However, previous work has not considered the interaction between temporal stimuli and the effect of emotions on the memory and recall processes of temporal stimuli. A memristor based temporal memory neural network circuit influenced by emotional arousal and memory interaction is designed in this paper. The circuit is composed of hippocampus module, memory interaction module, memory threshold module, emotional arousal module and temporal recall module. Firstly, when there is correlation between temporal stimuli, the interaction effect function between memories is realized through hippocampus module and memory interaction module. Secondly, the memory and recall processes of emotional arousal to temporal stimuli in different emotional states are realized through the emotional arousal module, hippocampus module and temporal recall module. Finally, the functions of memory fatigue, memory saturation and time-distance effect are realized. The simulation results of PSPICE verify the above functions. This circuit provides more reference for the further development of brain-like system.

In most memristive neural network circuits based on operant conditioning, the agent’s tendency towards certain behaviors is simply reflected through changes in synaptic weight. No specific analysis has been conducted on the changes in the tendency of intelligent agent behavior. Therefore, an operant conditioning circuit based on memristors and Q-learning is proposed to analyze the behavior and decision-making of intelligent agents in complex environments. The designed network uses Q-learning to update the decision voltage in the circuit based on the optimal Bellman equation, allowing agents to make different strategies according to the constantly changing environment to achieve optimal results. In addition, this study also uses the Double Q-learning to effectively reduce the overestimation bias of Q-learning and improve the stability and strategy performance of agent learning by introducing Q value estimation. This design provides more References for the development of automobile obstacle avoidance systems.

As the global energy structure shifts toward cleaner sources, wind power prediction has become increasingly important in modern energy management. Traditional prediction methods mainly rely on manual adjustment of model parameters and are constrained by hardware implementation conditions. The crossbar array structure of memristors can perform matrix operations directly in memory, offering significant energy efficiency advantages over traditional computing architectures. A hardware circuit design based on memristors for the improved red-billed blue magpie optimizer (IRBMO) and gate recurrent unit (GRU) is presented. The circuit includes the foraging module, the cooperation module, the mutation module and the GRU module. They realize the parallel computation of the prediction process. The circuit not only improves the computational efficiency, but also stores the optimal fitting value effectively. To further validate that the method is practical and effective, the simulation experiments are carried out in the short-term wind power prediction tasks. The results affirm the method high level of accuracy in short-term prediction tasks, which will provide a reference for the hardware implementation of neural network optimization.

With the long-term exposure to the subjectively recognized reward value, the sensitivity gradually weakens, which leads to the generation of incentive sensitization. A memristor-based neural network circuit is developed on the basis of memory reconsolidation theory, motivational sensitization theory, and emotional adaptability mechanisms. Its purpose is to inhibit memory retrieval, thereby extinguishing the incentive sensitization. In addition, this article considers the influence of inhibiting memory retrieval on the incentive sensitization under various emotional states. The designed circuit is primarily composed of a memory module, prefrontal cortex module, emotion module, amygdala module, inhibition module, and nucleus accumbens module. This circuit not only achieves the extinction of incentive sensitization induced by the inhibition of memory retrieval, but also exhibits inhibition effects under different emotional states, which had been verified through personal simulation program with integrated circuit emphasis (PSPICE) simulations. Finally, the proposed circuit provides a reference for modeling biological intelligence and industrial health applications, particularly the development of accurate health monitoring systems.

Most neural networks based on memristor only consider the relationship between emotion and memory, but ignore the relationship between emotion and directional forgetting. This article presents a neural network circuit based on memristors, which can achieve targeted forgetting with self-correlation and emotional memory. The designed circuit is mainly composed of memory module, self-correlation module, amygdala module, emotion module, and association neuron module. Memory module can dynamically adjust the time for memory formation and the time for memory decay according to the requirements of the task. Association between learning materials and the characteristics of the individual can be achieved through the self-correlation module. The learning material signals are transformed into positive learning material signal and negative learning material signal through the amygdala module. Emotion module and the association neuron module are used to determine whether the emotionality of the learning materials is consistent with one's own emotionality. The feasibility of the function of the proposed circuit is verified through Simulation Program with integrated circuit emphasis. This circuit provides more references for the further development of brain-like intelligence.

Most memristor-based nonassociative learning circuits only focus on habituation, while neglecting the research of sensitization and secondary sensitization. Therefore, this article designs a memristor-based sensitization learning circuit, which mainly consists of a habituation module, sensitization module, voltage control module, and secondary sensitization module. After receiving the signal from the voltage module, the habituation module and the sensitization module implement their functions, respectively. Second, this study investigates the influence of voltage stimuli on the formation rates of habituation and sensitization. Moreover, the functions of secondary sensitization and long/short-term sensitization processes have been successfully realized through the synergistic effect of the sensitization module and the secondary sensitization module. Finally, PSPICE software is used to verify the correctness of the design. The outcomes of simulations demonstrate that the circuit is capable of simulating the complex biological mechanisms mentioned earlier. Meanwhile, the circuit also realizes the application simulation of overheating protection for industrial equipment.

Memory homeostasis in the human brain's memory system is interactively influenced by circadian rhythms via neuroplasticity regulation and emotional states. In the current research field, there are still few hardware implementations that can couple rhythm-emotional dynamics similar to those in the brain. A memristor-based brain-inspired memory regulation circuit is proposed in this article. A synaptic weight dynamic balance model is constructed. The circadian rhythm mechanism is simulated under the circuit. In a waking state, short-term memory (STM) pathways should be established first. Memory integration is strengthened during sleep. The state-dependent regulation of neural plasticity is achieved. A 2-D emotional valence–arousal module and a cortisol concentration synaptic decay model are integrated into the circuit to simulate the mechanisms of long-term memory and STM suppression and decreased emotional stability caused by sleep deprivation. A time varying forgetting rate circuit combined with learning intensity monitoring is also designed. The memory regulation characteristics under different emotional states are monitored, such as forgetting compensation in negative high arousal states and memory weakening in low arousal states. Finally, the circuit is applied to intelligent industrial robots. The circuit provides a hardware implementation approach for the memory–emotion coupling mechanism in brain-inspired intelligent systems.

Learning rules are critical to the problem-solving ability of neural networks. Significant progress has been made in neural networks based on deoxyribonucleic acid (DNA) strand displacement (DSD). Traditional chemical reaction networks (CRNs) usually focus on the implementation of one type of learning rule. The coexistence of multiple learning rules remains challenging. In this article, CRNs based on DSD are constructed. The networks consist of a weight multiplication module, an activation function module, a learning signal module, a weight update module, and a weight output module. By exploring the concentration of auxiliary strands in the modules, discrete perceptron, Hebbian, and filtered learning rules are simulated successfully. The feasibility is verified through a simple instance. Modules are also used to build a classification model that can learn about thyroid disease and make predictions about test categories. The simulation is verified by the software Visual DSD. This article will provide a theoretical basis for biomedical prediction and identification.

Most researches on brain-inspired emotional circuits are grounded in memristors. Memcapacitors offer better static power consumption than memristors and exhibit dynamic characteristics that align well with biological neurons. In this article, we propose a memcapacitive circuit design for emotion modulation and congruency with schemas, anchored in the emotion classification model. The proposed circuit is rooted in the theories of schema memory and brain emotion, comprising essential components, such as sensory neuron modules, emotion neuron modules, and emotion-sensation association neuron modules. These modules emulate the functions of the hippocampus, amygdala, primary sensory cortex, and ventromedial prefrontal cortex in the brain. The circuit can receive sensory stimulation across five different frequencies, including visual, auditory, tactile, gustatory, and olfactory stimuli. Besides realizing multiemotional associative memories encompassing surprise, happiness, fear, anger, disgust, and sadness, this work also implements primary schema memory and can be applied to smart home control in Internet of Things based on prior knowledge, showcasing its versatility in practical and interdisciplinary contexts.

The operational integrity and rotational accuracy of bearings are critical in maintaining the reliability of precision devices within IoT systems. In order to improve the efficiency and accuracy of bearing fault diagnosis, a portable advanced bearing fault diagnosis model for IoT applications is proposed. It leverages a novel long short-term memory (LSTM) neural network architecture augmented with memristor technology for enhanced computational efficiency. In this work, a hardware neural network capable of running LSTM is designed, enabling low-power, fast and parallel computation. The circuit comprises three modules: 1) the weight calculation module; 2) the activation function module; and 3) the output module. The weights of the neural network are optimized and adjusted using the double-population jackal optimization algorithm. This algorithm performs convex lens imaging on the jackal population, applies reverse learning, and divides them into elite and ordinary jackals based on fitness values. It integrates the whale algorithm and cosine algorithm to strengthening the optimization ability of the jackal algorithm. Finally, the model is validated using the dataset from Paderborn University (PU). The results indicate that the accuracy of the model exceeds 96% for all four fault types. The findings underscore the potential of this model in powering the next generation of portable diagnostic tools for consumer electronics within the IoT framework.

Association is not a singular associative process, different emotional states affect associative responses. Most memristor-based multilateral associative circuits aim to optimize associative patterns, neglecting the combination of emotional states and bidirectional association. Based on the emotional association mechanism of brain, a memristor-based circuit for two-dimensional emotional recall and bidirectional associative relationships is proposed. The circuit model continuously modifies the storage and recall processes of emotional memory, persistently adjusting two-dimensional emotions and revealing their impact on bidirectional association and self-regulation. The circuit consists of the temporal lobe cortex module, amygdala module, hippocampus module, prefrontal cortex module and hypothalamus module. Emotional recall is achieved through the temporal lobe cortex module, amygdala module and hippocampus module. Two-dimensional emotions and emotional regulation are realized by the prefrontal cortex module. Emotional state responses are realized by the hypothalamus module. Ultimately, this circuit is applied to machine fault prediction, providing a new reference for bionic intelligent robot in the application of Internet of Things (IoT).

With the continuous advancements in computer technology and industrial technology, ensuring the security of industrial information is becoming more crucial. To safeguard against the exposure of sensitive industrial information, research into industrial image encryption technology is crucial. In this paper, a controllable multistable memristor model is presented. The multi-stability of the memristor is analyzed and described by mathematical model. The chaotic system of coupled multistable memristor is constructed using the Hopfield neural network (HNN) and the Hindmarsh-Rose neuron (HR). The intricate dynamic behavior of the HNN-HR chaotic system is uncovered through dynamic analysis and numerical simulations. The equivalent circuit of the HNN-HR chaotic system has been constructed, and the accuracy of the numerical results has been validated. The HNN-HR chaotic system has multi-stability and tunability of initial conditions, which can be used for industrial image encryption. The chaotic sequences generated by the HNN-HR system are utilized for encrypting industrial images by cyclic shift algorithm and bi-directional DNA diffusion algorithm. This paper provides an encryption scheme for the industrial Internet of things (IIoT). The research results indicate that the encryption schemes provide enhanced resistance to attacks. The encryption scheme shows great potential in the field of industrial image encryption and enhances the security of industrial image transmission.

Path planning for unmanned combat aerial vehicles (UCAV) has evolved into a multiconstrained, high-dimensional and multimodal optimization problem in complex combat environments. To solve the global optimal path planning problem of UCAV in a variety of complex terrain and multiple obstacles, human evolution optimization algorithm (HEOA) based on multistrategy is proposed in this article. In developed HEOA (DHEOA), a parallel population division combined with the double reverse learning strategy is employed to balance human exploration and development. Subsequently, the update strategies of the ball-rolling dung beetle and the thief dung beetle in dung beetle optimizer (DBO) are integrated into the human exploration stage. The ability for search is enhanced and convergence accuracy is improved. Finally, a variation strategy inspired by the natural development process is designed. The goal is to capture and activate the cycle of changes in population diversity. To evaluate the performance of DHEOA, four reference terraforms are generated from the real digital elevation model (DEM) and three different scenarios of each terraform are simulated. A series of path planning simulation experiments in a complex 3-D environment are carried out. The results show that the proposed algorithm can plan a path satisfying the constraints stably and efficiently. It has better results in UCAV path planning problems.

Most of the existing estimation methods for nonlinear systems have been developed by using non-self-error data (e.g., prediction error and observation error, etc.), potentially resulting in a tricky problem. In this paper, we propose a new estimator design for nonlinear Wiener-Hammerstein systems subject to quantised measurements, where the self-error data (i.e., initial error and estimation error) are used. For this purpose, the estimation error information is derived by introducing several auxiliary variables with an error feedback filter. Then, a compensated estimation error variable is designed to remove the hostile effect of the regressor matrix on the estimator. A novel adaptive parameter estimation learning law is proposed based on a performance evaluation function, where the compensated estimation error term, initial error term and several restraint conditions are used to construct the aforementioned evaluation function. In addition, the online verification of persistent excitation (PE) condition is also provided. Finally, the efficiency and availability of the proposed scheme are validated through numerical examples and experiment in comparison with the available estimation algorithms. Note to Practitioners—This study was motivated by the system modelling and identification problem of the servomechanism but is also uses to other nonlinear systems that have deadzone, saturation, backlash and hysteresis nonlinearity characteristics. Available methods to use common error data to establish an estimator that produces biased estimate and initial-value problems. This study introduces a new scheme using the self-error data to establish an estimator, to provide a new framework of identification method design, and to improve above-mentioned problems. The self-error data are directly related to parameter adaptive updates, thus giving positive estimation performance. In this study, we mathematically characterize the dynamical equations for the servomechanism. We introduce how to extract self-error data from the input and output data of system. This is the key step for us to construct an estimator in the future. Then, based on self-error data and several constraint conditions, a novel estimator is provided using recursive pattern. Preliminary practical experiments indicate that the proposed method is feasible but it has not yet been tested in the complex industrial production.

DNA computing has proven to possess strong parallel processing capabilities, offering notable advantages for multi-objective computation. Traditional, complex nonlinear DNA molecular logic circuits require the pre-construction of basic logic gates, followed by their cascading to achieve logic functions. However, as the number of cascade levels increases, more DNA strands are required to amplify and recover signals, causing the system's reaction time to grow exponentially. This paper introduces a novel approach for building complex nonlinear digital logic circuits using DNA winner-take-all neural networks. The logic circuit comprises four computational modules: weight multiplication, summation, competitive annihilation, and reporting. First, an annihilation strand is designed to control the reaction rate between two competing signals, resolving the interference between weight multiplication and competitive annihilation. Second, new grouping strategies—complementary annihilation, equal annihilation, and denoise annihilation—are introduced. These strategies exponentially reduce the number of competitive strands and significantly decrease system reaction time. The effect becomes more pronounced as the number of input patterns increases. Finally, 2-4, 3-8, and 4-16 decoder circuits are built using the winner-take-all neural networks, and an$\boldsymbol{n}\boldsymbol{-}\boldsymbol{2}^{\boldsymbol{n}}$universal decoder model is further developed. This study presents an effective method for implementing complex nonlinear logic circuits through DNA strand displacement reactions.

Researchers often focus on algorithmic enhancements while overlooking the potential benefits of hardware improvements. In this paper, a memristor-based parallel computing circuit optimization for LSTM network fault diagnosis is proposed. In response to the slow convergence of the algorithm, the characteristics of the memristor can matrix the algorithm and import it into the hardware circuit. The amnesia parallelization strategy executes four iterative processes simultaneously. The convergence speed is improved. Using the high-speed capability of the amnesia in parallel matrix operations using memristive circuits, four circuit modules are designed:mutation, crossover, evolution, and selection. These modules are integrated into a memristor circuit network model. To efficiently complete the iterative process and make effective use of the memristor’s strong storage property, the best-fit values are stored. To validate the effectiveness of the algorithm, simulations and comparative experiments are conducted on the Case Western Reserve University (CWRU) dataset. The results show that the model optimised with memristor hardware circuitry has improved the accuracy by 98% and has better fault diagnosis performance. This research not only advances the integration of memristive devices in neural network optimization, showcasing significant implications for the design of advanced circuit systems in the era of intelligent computing.

Operant conditioning (OC) and secondary conditioning are two important mechanisms for organisms to adapt to external environments. However, most memristor-based neural network circuits only consider a single OC. The secondary conditioned reflex process that occurs on the basis of OC during actual animal training are ignored. On the basis of memristors, the operant conditioning neural network circuit with emotion transmission and secondary conditioned reflex is designed. After OC learning, organisms respond to the first conditioned stimulus. On this basis, another conditioned stimulus is introduced and secondary conditioned reflex is indirectly established. Additionally, the phenomenon of emotion transmission and the effect of emotion on associative memory are considered. The designed circuit mainly consists of delay module, voltage control module, emotion transmission module and synaptic module. The implementation of the secondary conditioned reflex process based on OC is achieved through the delay module, voltage control module and synaptic module. Emotion transmission and the influence of emotion on associative memory are realized by emotion transmission module and voltage control module. The PSPICE simulation results confirm the implementation of the above functions. This circuit provides more references for the further development of brain-like technology.

With the rapid development of intelligent information technology, it is significant to construct neural network models that conform to biological characteristics. In this paper, a memoristor model with tunable multistable properties is proposed. By changing the memory parameters, the number of multistable states can be adjusted. Based on the memoristor, an asymmetric memristive FN-HNN neural network (MFNHNN) containing five neurons is constructed. The fundamental dynamical theories, such as equilibrium points, bifurcation diagrams and Lyapunov exponents, are used to reveal the complex dynamic behaviours of MFNHNN. The different dynamic behaviors with coupling intensity control, the tunable coexistence of infinite chaotic attractors and the coexistence of initially controlled chaos and periodic attractors are observed. Furthermore, the equivalent circuit of MFNHNN is implemented. On the basis of random chaotic sequences, an image encryption scheme combining Arnold mapping and diagonal diffusion algorithm is proposed. The findings indicate that the proposed scheme exhibits superior encryption performance, rendering it suitable for application in remote sensing information security.

Current memristive circuits only focus on the generation of emotion, without considering the evolution and decay process of emotion. In this paper, a memristive circuit that can more efficiently realize the generation of emotion is designed, which also takes into account the evolution and decay of emotion. The designed circuit mainly consists of signal processing module, logic selection module, emotion generation module, personality module and emotion expression module. The composite signals are generated through different tactile, olfactory and gustatory stimuli. The combination of different tactile, olfactory and gustatory stimuli can produce six different control signals through the logic selection module. The emotion generation module achieves the generation and evolution of emotions, while also considering the process of emotion decay under different personality traits. Finally, a three-dimensional pleasure arousal dominance (PAD) emotional space is proposed, which can express continuous emotions according to pleasure signals, arousal signals and dominance signals. The feasibility of the circuit is verified by PSPICE. The proposed circuit may provide some references for the development of artificial emotion.

Biological circuits can not only be applied to ultra sensitive biomedical testing, but also provide new ideas for research in fields such as biomolecular information control, secure communication, and biological computers. In recent years, the synchronization of biological chaotic circuit (BCC) based on DNA strand displacement (DSD) has been widely studied and applied in the field of biological information secure communication. Therefore, this paper proposes a Q-S synchronization scheme of BCC based on DSD, and applies it to biological information secure communication. First, through the research and analysis of DNA molecular reaction dynamics and dual-rail representation, the ideal chemical reaction networks (CRNs) are realized, and the BCC is achieved through the cascade of CRNs. Second, the CRNs of synchronization controller are constructed according to the construction method of Q-S controller, and the combined synchronization between different variables of two BCC with different orders is realized. Finally, CRNs of biological information are designed, and secure communication and decryption of biological information are realized under Q-S synchronization scheme. The effectiveness and robustness of the scheme are proved by numerical simulation in software Visual DSD and MATLAB. Our work provides a new reference for the synchronization of BCC and the secure communication of biological information.

Current biological behavior models only take the external environment information as the basis for decision-making, ignoring the internal emotional state information. A memristor-based cerebellar model articulation controller (CMAC) neural network circuit of artificial fish behavioral decision is designed, and fuzzy emotion is taken into account. The designed circuit is mainly composed of voltage selection modules, fuzzy processing modules, synaptic neuron modules, eigen quantity modules and feedback modules. CMAC neural network is used as learning criteria and the learning subspace voltage with emotional generalization properties outputs to synaptic neural module. By utilizing the nonvolatility and thresholding properties of the memristor, the weights in the neural network are changed to enable the artificial fish to perform primary and secondary learning under specific emotional voltages. The feasibility of the above circuit is verified by PSpice simulation software. The artificial life and biological intelligence behavior are integrated by the memristor-based CMAC neural network circuit. It provides a reliable theory and basis for the emotional behavior of bionic robots.

From the perspective of time, the generation of emotion has not only emotional generalization but also emotional differentiation. Most studies of emotional systems only consider the present tense, and the past and future tenses are not considered. In this article, based on the bionic multiloop emotion learning model, a memristor-based feature recall neural network circuit with temporal differentiation of emotion is proposed. The circuit is composed of thalamus module, insular cortex module, anterior cingulate cortex module, sensory cortex module, amygdala module, and feature recall module. Feature recall module and emotion learning circuit are used to combine feature associative memory with emotion generation. Temporal differentiation of emotions is realized. The multiloop affective learning circuit also takes into account the activation patterns of different brain regions in response to positive and negative stimuli. The feasibility of the above circuits is verified by PSpice simulation software. The feature recall neural network circuit with temporal differentiation of emotion provides further reference for the bionic robot to realize inference prediction function.

Conditioning and associative memory play an important role in the learning process of biological brain, and many neural network circuits have been designed to reproduce the relevant classical experiments. However, these circuits are mainly devoted to the realization of various phenomena in the acquisition process, such as reacquisition, generalization, differentiation, and blocking. A Pavlov associative memory circuit based on memristor is designed in this article. The circuit realizes latent inhibition effect and a variety of biological forgetting features. The correctness of implementing these functions described above is demonstrated by simulation results in Pspice. This circuit can realize the influence of external environment changes on the learning and forgetting states of organisms. It offers valuable insights for modeling biological intelligence and simulating the learning function of human associative memory. Particularly, associative memory and transient forgetting can be applied to industrial intelligent grasping robots.

Nowadays, memcapacitor-based associative memory neural networks are focusing on classical conditioning roles and ignoring operant conditioning roles. In this article, a biomimetic model of operant conditioning neural network based on memcapacitor is designed. The designed circuit includes neuron module, time delay module, hunger output module, experience module, and decision making based on experience module. The novel neural network based on memcapacitors implements learning, forgetting, immediate and delayed reinforcement learning, blocking, generalization, and decision making. In addition, the effects of hunger and satiety on operant conditioning are discussed and implemented using memcapacitors to represent states of deprivation. PSPICE simulation results show that the circuit can be used to simulate real-world conditioned reflexes and complex applications. The proposed circuit can be applied to an intelligent inspection robot for power distribution rooms, enabling autonomous learning and equipment detection.

Current memristive circuits for biological decision-making only consider simple situations and do not take into account how the organisms themselves learn these behaviors. In this article, a memristor-based generalized fuzzy min–max (GFMM) neural network circuit of biology with multiobjective decision is designed, imprinting learning is taken into account. The designed circuit is mainly composed of imprinting learning module, generalization and differentiation learning module, multimodal learning module, behavioral decision module, graded response and feedback module. Behavioral signals are converted into high-intensity and low-intensity learning signals by imprinting learning module, which are output to multimodal learning module for multiple processes. The generalization and differentiation learning module is designed to better analyze the learning signals. Multiple factors are processed by behavior decision module, different behaviors are output based on GFMM neural network. The feasibility of the circuit is verified by PSpice, which provides a reference for biomimetic robots in learning, decision-making, and industrial firefighting.

Current memristive circuits only focus the impact of simple rewards and punishments on biological behaviors, without considering the consequences of sustained stimuli and the occurrence of secondary behaviors. In this article, a memristor-based reward and punishment neural network circuit with approach and inhibition is designed, secondary behaviors are taken into account. The designed circuit is mainly composed of thalamus module, reward pathway, punishment pathway, amygdala module, feature module, and prefrontal cortex module. The signal processing in the brain is simulated by reward and punishment neural network, where signals of different intensities are produced to generate different overshadowing effects. Continuous stimulus is generated by external signal, producing different emotions and affecting memory. Approach and inhibition behaviors are initial outcomes, followed by secondary behaviors by competition between systems. The feasibility of the circuit is verified by PSpice, the proposed circuit provides a reference for biomimetic robots in neurocomputing and industrial applications.

The traditional association theory maintains that associations between cues can change only in trials where the cue is actually presented. However, the retrospective revaluation (RR) studies the phenomenon that responses to a cue can change even when the cue is not actually presented. A hardware memristor-based neural network circuit with an RR effect is proposed in this article. The neural network circuit successfully demonstrates various phenomena of RR, including the impact of deflation and inflation of companion cue associations on target cue, higher order RR, and context dependence. The correctness of the circuit design is verified by Pspice simulation. The key feature of this design lies in its ability to learn cue associations even in training trials, where the target cues are absent. This distinctive attribute offers a fresh perspective for the creation of more intricate, brain-inspired information processing systems with enhanced integration capabilities.

Pavlovian associative memory plays an important role in our daily life and work. The realization of Pavlovian associative memory at the deoxyribonucleic acid (DNA) molecular level will promote the development of biological computing and broaden the application scenarios of neural networks. In this article, bionic associative memory and temporal order memory circuits are constructed by DNA strand displacement (DSD) reactions. First, a temporal logic gate is constructed on the basis of DSD circuit and extended to a three-input temporal logic gate. The output of temporal logic gate is used for the weight species of associative memory. Second, the forgetting module and output module based on the DSD circuit are constructed to realize some functions of associative memory, including associative memory with simultaneous stimulus, associative memory with interstimulus interval effect, and the facilitation by intermittent stimulus. In addition, the coding, storage, and retrieval modules are designed based on the analysis and memory capabilities of temporal logic gate for temporal information. The temporal order memory circuit is constructed, demonstrating the temporal order memory ability of DNA circuit. Finally, the reliability of the circuit is verified through Visual DSD software simulation. Our work provides ideas and inspiration to construct more complex DNA bionic circuits and intelligent circuits by using DSD technology.

In recent years, biological neural networks have developed rapidly due to their advantages of fast parallel computing processing speed and strong fault tolerance. This article is dedicated to explore innovation in this field and successfully constructing a Hopfield neural network model based on DNA strand displacement (DSD) circuits. First, this article constructs four core functional modules based on DSD, including an encoder module, weighted sum module, comparator module, and decoder module. These functional modules together form the design foundation of the DSD circuit, achieving effective circuit construction. Second, the construction of the Hopfield neural network is achieved through DSD circuits. The construction of this network achieves the integration of DSD technology and neural networks. Finally, the Sudoku conjecture problem is solved through the neural network. This article conducts a simulation in visual DSD, which verifies the feasibility of Sudoku conjecture. Our work integrates DSD technology with neural networks and uses them to solve practical problems. This fusion broadens the research field of neural networks and demonstrates the potential of biotechnology in practical applications.

In the field of biocomputing and neural networks, deoxyribonucleic acid (DNA) strand displacement (DSD) technology performs well in computation, programming, and information processing. In this article, the multiplication gate, addition gate, and threshold gate based on DSD are used to cascade into a single DNA neuron. Multiple DNA neurons can be cascaded to form different neural networks. The DNA neural networks are designed to implement seven classical conditioned reflexes from Pavlovian associative memory experiments. A classical conditioned reflex is a combination of a conditioned stimulus (CS) and another un CS with a reward or punishment. So that the individual develops a conditioned reflex that is similar to an unconditioned reflex in the use of CS alone. The seven classical conditioned reflexes include acquisition and forgetting, interstimulus interval effect, blocking, conditioned inhibition, overshadowing, generation, and differentiation. The simulations are verified by the software visual DSD. This article provides a direction for the integration of biology and psychology.

Most memristor-based neural network circuits consider only a single pattern of overshadowing or emotion, but the relationship between overshadowing and emotion is ignored. In this article, a memristor-based neural network circuit of associative memory with overshadowing and emotion congruent effect is designed, and overshadowing under multiple emotions is taken into account. The designed circuit mainly consists of an emotion module, a memory module, an inhibition module, and a feedback module. The generation and recovery of different emotions are realized by the emotion module. The functions of overshadowing under different emotions and recovery from overshadowing are achieved by the inhibition module and the memory module. Finally, the blocking caused by long-term overshadowing is implemented by the feedback module. The proposed circuit can be applied to bionic emotional robots and offers some references for brain-like systems.

Different emotional states have different impacts on the emotional system. To mitigate these impacts, the emotional system will maintain the stability of the system through various adjustment methods, such as emotional self-regulation. However, there is no hardware circuit to realize the regulation of the emotional system in different emotional states. In this article, based on the brain emotion theory of the limbic system, a memristor-based emotion regulation circuit is proposed. The circuit is composed of the thalamus module, sensory cortex module, amygdala module, and orbitofrontal cortex module. It can not only produce corresponding emotions according to different stimuli but also output and regulate the current emotional state. The function of emotion generation and emotion output is realized through the thalamus module, sensory cortex module, and amygdala module. Emotional self-regulation, emotional generalization, mood disorder and mood disorder regulation are realized through the combined action of the amygdala module and the orbitofrontal cortex module. Finally, the emotion regulation circuit composed of the memristor is applied to the fault robot monitoring, which provides a reference for the bionic intelligent robot in the actual industrial application scenario.

Signaling activity in the cerebral cortex is corrected by inhibition and blocking to make neural response processes more precise and efficient, inhibition and blocking are positive neural processes. In this article, a memristor-based conditioned inhibition neural network circuit with blocking generalization and differentiation is proposed. The designed circuit consists of the voltage control module, synapse neuron module, inhibition module, blocking module, and generalization module. Through the cooperative action of the neuronal associative learning and the inhibition module, the neutral conditioned stimulus is transformed into a conditioned stimulus with inhibitory properties to achieve conditioned inhibition. The influence of unconditioned stimulus on synaptic weight is considered during associative learning, and unblocking is realized by using the blocking module. Based on the blocking module and the generalization module, blocking, generalization, and differentiation are combined to realize blocking generalization and differentiation. The PSPICE simulation verifies the feasibility of the above functions. Conditioned inhibition neural network circuit with blocking generalization and differentiation provides a reference for the further development of brain-like technology.

The Internet of Things has shown great advantages in intelligent healthcare applications. In this article, based on the idea of Internet of Things application in intelligent healthcare, the channel of connection between thyroid gland and DNA strands is established so that the information can be delivered. Tracking and control of the thyroid feedback network (TFN) can be achieved through variations of DNA strand concentration. First, DNA strand displacement chemical reaction networks (DSD CRNs) of the thyroid three-compartment network model [thyrotropin releasing hormones (TRHs), thyrotropin stimulating hormones (TSHs), and thyroid hormones (THs)] are constructed based on DSD. Second, the variations in biochemical indicators of thyroid-related hormones (TRH, TSH, and TH) are analyzed by studying the dynamic behavior of the TFN. The changes in thyroid-related hormones reflect to the symptoms of thyroid-related diseases (TRDs). Third, the CRNs of the active tracking controller are constructed to achieve dynamic equilibrium of biochemical indicators in the TFN. Therefore, the regulation of the TFN is implemented. The Visual DSD and MATLAB software are used to verify the robustness and effectiveness of the TFN. The TFN can better predict biochemical indicators in patients with TRDs.

IoT technology is a key driver for many applications in different fields, such as digital health, smart city, industrial automation, and supply chain. The information security of the Internet of Things is one of the important requirements. Due to the extreme sensitivity to initial values, high randomness and unpredictability of chaotic information, chaotic synchronization is widely used in the field of IoT information encryption. In this article, chaotic systems are used as information security transmission systems for the IoT, and a IoT information encryption scheme for chaotic synchronization under reduced-order observer is proposed. First, through the study of DNA molecular reaction dynamics, the IoT information encryption of chaotic synchronization is extended to the field of DNA strand displacement (DSD). Second, according to dual-rail theory, the chemical reaction networks (CRNs) of IoT information security transmission systems and IoT transmission information are constructed by multiple DSD reaction modules. Finally, CRNs are cascaded to realize the encryption and decryption of IoT information. Through numerical simulation in Visual DSD and MATLAB, the phase diagram, sequence diagram, Lyapunov exponent diagram and bifurcation diagram are given, and their dynamic characteristics are analyzed. Simulation results verify the effectiveness and feasibility of the scheme. Our work will provide a new reference scheme for chaotic synchronization and IoT information encryption using DSD.

Most associative memory neural networks are realized by memristor, but memcapacitor which can simulate the biological behavior of neuron preferably has better characteristics than memristor to realize the pavlov associative memory neural networks. This article introduces a novel neural network paradigm with memcapacitors, encompassing thirteen classical conditional reflection functions. These include pivotal aspects such as learning, forgetting, time interval conditioning, latent inhibition, time delay conditioning, facilitation, blocking, secondary conditioning, and fear learning, meticulously validated through simulation results. The proposed architecture interconnects nine analogous neuron modules through diverse synapses, culminating in a meticulously designed circuit. This memcapacitor biomimetic circuit not only achieves the implementation of thirteen classical conditional reflections but also boasts scalability, offering versatility in its application. Particularly noteworthy is its potential application in marine debris collection robots, showcasing adaptability in working intricate oceanic traffic conditions.

Most memristor-based neural network circuits consider only a single pattern of classical conditioning (CC) or operant conditioning (OC), but the simultaneous occurrence of CC and OC during actual animal training is ignored. In this paper, a memristor-based neural network circuit of operant conditioning with bridging and conditional reinforcement is designed. CC and OC can occur simultaneously and multiple CC and OC functions are considered. The designed circuit mainly consists of memory module, delay module, prefrontal cortex module, experience module and generalization module. Bridging in OC is implemented by the delay module and the prefrontal cortex module. Conditional reinforcement in OC is realized by the memory module and the prefrontal cortex module. Finally, the generalization of OC is achieved through the generalization module and the experience module. The proposed circuit may inform the study of smarter brain-like systems.

Picture Archiving and Communication System (PACS) is an important technology for the transmission of medical images and related information. Using the PACS technology, medical images can be transmitted and shared over local area networks, wide area networks. However, ensuring transmission security continues to be crucial challenges in the field of modern medicine. In this paper, a hyperbolic memristor model with different hysteresis loops for different input signals is proposed, which has both locally active and passive properties under different parameters. Based on the memristor model, a four-neuron Hopfield neural network (HNN) is introduced, incorporating a memristor as a replacement for one of the synaptic weights, while subjecting one of the four neurons to electromagnetic radiation. The effects of the memristor parameters and coupling weights on neural network are studied, which reveals the existence of coexistence behavior in neural network. In addition, an equivalent circuit of HNN is implemented to demonstrate the accuracy of the dynamical analysis. Finally, a medical image encryption approach with bi-directional dynamic permutation and multi-directional dynamic diffusion process is proposed. The experimental findings demonstrate that the encryption scheme has strong robustness, which can be applied in PACS to enhance the security of medical images during transmission.

With the continuous development of computers, communication technology, and regional medical collaboration services, the security and confidentiality of information are becoming more and more important. In order to prevent the illegal leakage of sensitive patient information, it is of great significance to study medical image encryption. In this article, a flux-controlled hyperbolic memristor model with locally active characteristics is proposed, which has rich nonlinear characteristics. The memristor parameters affect the local activity of the memristor, which is explained by mathematical analysis. Based on the traditional hopfield neural network (HNN), a memristive fully connect neural network (MFNN) containing four neurons is constructed with more complex coupling relationships between individual neurons. The memristor can be used to characterize the effect of external electromagnetic radiation on neurons. The complex dynamical behaviors of MFNN are found by numerical simulations. An equivalent circuit for the neural network is constructed to verify the accuracy of the numerical simulation. In addition, a medical image encryption scheme based on MFNN is proposed. The encryption scheme performs a bit-level permutation of the original image using a chaotic sequence randomly generated by the chaotic system. Fibonacci$Q$-matrix and central diffusion algorithm are used to diffuse the permutation image. Through numerical analysis, the maximum entropy of this encryption algorithm reaches 7.99, and the correlation is close to zero, which proves the resistance of the algorithm to statistical attacks. The algorithm takes only 3.9 s to encrypt an 8-bit medical image of 320 × 320 size on Windows 10 operating system. Experimental results show that the proposed encryption scheme is very secure and has good applications in medical image encryption.

Operant conditioning is an important learning mechanism for organisms, as well as a basic theory for reinforcement learning in artificial intelligence. Although there are already some memristive neural circuits for operant conditioning, they can only process a single stimulus and cannot handle multiple inputs simultaneously. This article proposes a multi-input operant conditioning neural network that incorporates blocking and competing effects. This network can achieve the blocking and overshadowing effects in the presence of multiple inputs and learn efficiently in complex environments. In addition, it incorporates time differences between signals and excitations, random exploration, feedback learning, experience memory, decision-making based on experience, and adaptive learning in low-reward environments. Finally, the feasibility of the proposed circuit function is verified through PSPICE simulation. This work provides an implementation idea for the hardware implementation of artificial intelligence.

A memory mechanism has attracted growing popularity in tracking tasks due to the ability of learning long-term-dependent information. However, it is very challenging for existing memory modules to provide the intrinsic attribute information of the target to the tracker in complex scenes. In this article, by considering the biological visual memory mechanisms, we propose the novel online tracking method via an attention-driven memory network, which can mine discriminative memory information and enhance the robustness and reliability of the tracker. First, to reinforce effectiveness of memory content, we design a novel attention-driven memory network. In the network, the long memory module gains property-level memory information by focusing on the state of the target at both the channel and spatial levels. Meanwhile, in reciprocity, we add a short-term memory module to maintain good adaptability when confronting drastic deformation of the target. The attention-driven memory network can adaptively adjust the contribution of short-term and long-term memories to tracking results under the weighted gradient harmonized loss. On this basis, to avoid model performance degradation, an online memory updater (MU) is further proposed. It is designed to mining for target information in tracking results through the Mixer layer and the online head network together. By evaluating the confidence of the tracking results, the memory updater can accurately judge the time of updating the model, which guarantees the effectiveness of online memory updates. Finally, the proposed method performs favorably and has been extensively validated on several benchmark datasets, including object tracking benchmark-50/100 (OTB-50/100), temple color-128 (TC-128), unmanned aerial vehicles-123 (UAV-123), generic object tracking -10k (GOT-10k), visual object tracking-2016 (VOT-2016), and VOT-2018 against several advanced methods.

The 1-D and 2-D emotion models realized by hardware circuits have been studied. However, 3-D emotional model which is most suitable for human emotion has not been considered. In this article, a bionic circuit of 3-D emotional space model is proposed, which can generate brain-like emotions according to the information of visual, speech, and text. The designed memristor circuits are based on the brain emotion theory of limbic system, including thalamus, sensory cortex, orbitofrontal cortex, cingulate gyrus, amygdala, and other circuit modules. Moreover, the perceptual brain and rational brain in the memristor circuits are also considered. The 3-D model in this article is a PAD emotional space model composed of three dimensions: 1) pleasure (P); 2) arousal (A); and 3) dominance (D). Many emotions can be expressed by using the PAD emotional model. In addition, the PAD emotional model composed of memristor circuits is applied to mood congruity, which considers the relationship between emotion and learning. The PAD emotion designed by memristor circuits may provide a reference for bionic robot to realize human–computer emotional companionship.

The complete synchronization of two chaotic systems and its secure communication have been considered based on DNA chemical reaction networks (DNA CRNs). There are concerns about the security of the single-level chaotic secure communication system. Thereby a multilevel secure communication scheme is put forward via DNA CRNs in this work. First, a three-variable chaotic system that consists of catalysis, double, fasciation, and annihilation modules is constructed using DNA CRNs. Second, according to the stability principle of nonlinear systems, DNA CRNs are adopted to realize the coupling projection synchronization of three chaotic systems, and the synchronization results are discussed. Finally, sine and cosine signals are designed using DNA CRNs. These signals are added into three systems to realize multilevel encryption and decryption. Furthermore, the interference terms are taken into consideration to test the robustness of the system. The results show that the proposed scheme has high security, and can efficiently encrypt and decrypt biological signals through multilevel transmission in the presence of interference.

Most memristor-based neural networks only consider habituation under repeated stimuli, but the emotional habituation under repeated emotional stimuli is ignored. In this article, a memristor-based neural network circuit that can realize emotional habituation with contextual dependency is proposed. The designed circuit consists of habituation module, emotion control module, context control module, and generalization module. When different emotional stimuli are applied, the emotion control module produces different feedback voltages and has an impact on the formation of habituation. In addition, the influence of contextual information on habituation is considered and the contextual dependency of habituation is achieved. Finally, the generalization of habituation is achieved through the generalization module. The simulation results in PSPICE indicate that the proposed circuit can realize emotional habituation with contextual dependency. The neural network circuit of emotional habituation with contextual dependency provides some references for brain-like intelligence.

In this article, a general memristor model emulator based on the hyperbolic function is proposed. Based on the mathematical model of hyperbolic function memristor, a general circuit model emulator is designed. The general circuit can realize memristor models of different hyperbolic functions by controlling the state of switches in the circuit. The advantage of the emulator is that it can realize three different mathematical models of hyperbolic function and be easily integrated. The circuit of the hyperbolic function memristor model conforms to three basic characteristics of the mem-element, so it can be regarded as a memristor element. Finally, the model of charge-controlled memristor based on hyperbolic function is derived. The universal memristor model proposed in this article enriches the research of constructing memristor components with existing components and provides a reference for future research on mem-element emulators.

Currently, the research in memristor-based associative memory neural networks pays more attention to classical conditioning and lays less attention to operant conditioning. Moreover, a single reinforcing stimulus is applied in the most studies of operant conditioning. In this paper, a memristor-based duple-stimulus operant conditioning circuit is designed. The circuit utilizes duple stimuli which include two stimuli and realizes the effect of stimuli with two intensities on operant conditioning. In addition, the circuit is applied to a memristor-based neural network circuit of duple-reward and duple-punishment operant conditioning with time delay. The application circuit realizes duple-reward and duple-punishment operant conditioning with time delay on the basis of duple-stimulus operant conditioning circuit. Meanwhile, the factors that affect operant conditioning such as satiety and the immediacy of stimuli are implemented in the circuit. Different delays for duple-reward module and duple-punishment module are designed. The application circuit is closer to the practical application and is more consistent with biological characteristics. It provides more references for neural networks of operant conditioning with further development.

The reinforcement and extinction in conditioned reflex have been studied extensively, but memristor-based generalization and differentiation circuits under different emotional conditions are rarely studied. Therefore, a memristor-based generalization and differentiation circuit under positive and negative emotional conditions is presented in this paper. The circuit includes emotion module, synapse module, voltage selection module and output module. The emotion module is divided into positive emotion and negative emotion modules. Different emotions have different effects on the synapse module, which in turn affects the output module. The memristor-based circuit proposed in this paper can not only realize the process of generalization and differentiation under the influence of different emotions, but also realize the function of secondary differentiation. The results presented in this paper can be verified in PSPICE. By analyzing the effects of different emotions on differentiation and generalization, this paper provides some references for future researches in the field of generalization and differentiation.

Classical three-variable chaotic system coupling synchronization has been implemented in previous work based on DNA strand displacement (DSD). Herein, by using DSD reactions as the foundation, a proportional integral (PI) controller for chaotic system is introduced to realize the hybrid projective synchronization for different four-variable chaotic systems. DSD-based chaotic systems are composed of catalysis modules, annihilation modules and degradation modules for realizing the construction of chaotic attractors. PI controllers are consist of catalysis, annihilation and adjust DSD modules that are easy to modify and can be added to chaotic system for achieving hybrid projective synchronization. Our work can be acted as the reference for the investigation of chaos synchronization.

Most of the classical conditioning laws implemented by existing circuits are involved in learning and forgetting between only three neurons, and the problems between multiple neurons are not considered. In this article, a multimode generalization and differentiation circuit for the Pavlov associative memory is proposed based on memristors. The designed circuit is mainly composed of voltage control modules, synaptic neuron modules, and inhibition modules. The secondary differentiation is accomplished through the process of associative learning and forgetting among multiple neurons. The process of multiple generalization and differentiation is realized based on the nonvolatility and thresholding properties of memristors. The extinction inhibition and differentiation inhibition in forgetting is considered through the inhibition modules. The Pavlov associative memory neural network with multimodal generalization and differentiation may provide a reference for the further development of brain-like intelligence.

The emergence of DNA strand displacement has prompted the development of chaotic synchronization techniques, and previous works mainly focus on the study of synchronization for two three-dimensional chaotic systems via DNA strand displacement. In this article, four-dimensional (4-D) chaotic systems and loop controllers are designed using several strand displacement units, and loop synchronization for three 4-D chaotic systems is achieved by cascading the designed chaotic systems and controllers. It is revealed from the results of Visual DSD that strand displacement reactions can realize the loop synchronization of three 4-D nonlinear chaotic systems, and our method has robustness when one loop controller does not work. Our works will provide reference for the investigation of chaotic synchronization using DNA techniques.

Most memristor-based associative memory neural networks are focused on classical conditioning and ignored operant conditioning. In this paper, a memristor-based neural network of operant conditioning accorded with biological feature is designed. The designed circuit includes a voltage control module, an operant module and synapse modules. It realizes learning, forgetting, long-term memory, reinforcement and punishment functions based on variable synapse structure and double self-protection measure. Meanwhile, the four factors that affect operant conditioning such as contingency, immediacy, magnitude and deprivation are discussed and implemented. The simulation results in PSPICE show that the circuit can be used to simulate actual conditioned reflex and complicated applications. The memristor-based neural network circuit of operant conditioning provides more references for further development of neural networks.

Numerous tracking approaches attempt to improve target representation through target-aware or distractor-aware. However, the unbalanced considerations of target or distractor information make it diffcult for these methods to benefit from the two aspects at the same time. In this paper, we propose a target-distractor aware model with discriminative enhancement learning loss to learn target representation, which can better distinguish the target in complex scenes. Firstly, to enlarge the gap between the target and distractor, we design a discriminative enhancement learning loss. By highlighting the hard negatives that are similar to the target and shrinking the easy negatives that are pure background, the features sensitive to the target or distractor representation can be more conveniently mined. On this basis, we further propose a target-distractor aware model. Unlike existing methods of preference target or distractor, we construct the target-specific feature space by activating the target-sensitive and the distractor-silence feature. Therefore, the appearance model can not only represent the target well but also suppress the background distractor. Finally, the target-distractor aware target representation model is integrated with a Siamese matching network for visual tracking for achieving robust and realtime visual tracking. Extensive experiments are performed on eight tracking benchmarks show that the proposed algorithm achieves favorable performance.

Most memristor-based Pavlov associative memory neural networks strictly require that only simultaneous food and ring appear to generate associative memory. In this article, the time delay is considered, in order to form associative memory when the food stimulus lags behind the ring stimulus for a certain period of time. In addition, the rate of learning can be changed with the length of time between the ring stimulus and food stimulus. A memristive neural network circuit that can realize Pavlov associative memory with time delay is designed and verified by the simulation results. The designed circuit consists of a synapse module, a voltage control module, and a time-delay module. The functions, such as learning, forgetting, fast learning, slow forgetting, and time-delay learning, are implemented by the circuit. The Pavlov associative memory neural network with time-delay learning provides a reference for further development of the brain-like systems.

In this paper, a new chaotic system is proposed, whose dynamical behaviors are discussed with the change of the parameters in detail. The specific effects of different parameters on the system are also discussed. By adjusting these parameters of the proposed circuit, this nonlinear circuit can prod uce the different dynamical behaviors, such as, hyper chaotic behavior, periodic behavior, transient behavior, etc. Furthermore, a novel kind of modified compound synchronization has been investigated, where the multiple chaotic systems have been considered for different combination modes: the compound system of four scaling drive systems and one response system. The corresponding controllers are designed to realize the modified compound synchronization. The theoretical proofs and numerical simulations are given to demonstrate the validity and applicability of the proposed chaotic system and the modified compound synchronization.

Kernelised correlation filter (KCF)‐based trackers have recently attracted considerable attention due to their exciting accuracy and efficiency. Numerous improvements have been made later for coping with scales variation or partial occlusion etc . However, when there is an abrupt motion between the consecutive image frames, these trackers would face failure. To alleviate the problem, the authors present an extended cuckoo search (CS)‐based KCF tracker (called ECSKCF). At first, the extended CS algorithm is constructed by the Simplex method (SM). CS has obvious capability in global search while the SM has exceptional advantage in local search. Based on ECS method, motion prediction is transformed to globally search for optimal position intending to enhance the quality of base image. Then, combined ECS with Gaussian distribution, a hybrid motion model is introduced to KCF framework, which has the capability of capturing abrupt motion. Finally, a unified framework is designed to track smooth or abrupt motion simultaneously. Extensive experimental results in both quantitative and qualitative measures demonstrate the effectiveness of the authors’ proposed method for abrupt motion tracking.