In this study, we design and test a complex network using our chaotic circuits with memristors that switch between periodic and chaotic oscillations. As a result, we observe the simultaneous presence of both periodic and chaotic oscillations, alongside instances where switching between these oscillation types occurs within the proposed complex network. In addition, we confirm that the synchronization state and the dynamics of the memristor change with coupling strength. Furthermore, this study investigates the ratio of periodic and chaotic oscillations by changing the memristor parameters to clarify the effect of memristors in the complex network.

Peristaltic pumps are widely used in many industrial applications, especially in medical devices. Their reliability depends on proper maintenance, which includes the total replacement of tubes regularly due to the aging of the materials. The proper use of predictive maintenance techniques could potentially improve the efficiency of maintenance interventions and prevent failures by having a way to determine when the tube has passed its replacement time. We recorded a dataset using six different sensors (three accelerometers, one gyroscope, one magnetometer, and one microphone) using several cassettes (three new units and three units with expired life span). The recording was done at the highest possible frequency (100–6667 Hz, different for each sensor) and then downsampled several times to obtain frequencies as low as 12 Hz. This dataset is now publicly available. We trained 939 different models, which were the result of combining all different sensors as inputs but the microphone, and four basic architectures of recurrent neural network: One or two layers of either gated recurrent unit or long short-term memory with different number of nodes per layer (from 2 to 64). Among all trained models, we selected the ten best performing networks in terms of both accuracy and complexity. All of them reached an F1 score of 0.99 or 1 with holdout cross-validation. Those models were deployed on four different edge AI devices. For all combinations of model and edge AI devices we obtained metrics of memory size (from 0.3% to 160.6% RAM, and from 0.9% to 21.3% flash), inference time (from 0.39 to 1463.91 ms), and average consumption (from 0.15 to 5.30 mA). Nine out of ten models were proven viable for deployment. We concluded that the four models based on magnetometer data were significantly better in terms of consumption and inference time. To the best of our knowledge, the use of magnetometer data is a very uncommon approach to failure detection in predictive maintenance applications, and this is probably the first time it has been used for peristaltic pump aging detection, so our results are very promising for future applications. Also, since most trained models use little resources, we have proved that our approach is perfectly compatible with running other communication and control algorithms on the same device, which is ideal for easy integration and scalability in industrial systems. Some limitations for real deployment include facing environmental factors (noise) and long-term monitoring, so we also proposed a protocol that should reduce the impact of those factors by taking measurements in a controlled way.

In this study, we applied sparse coupling to the coupling system of van der Pol oscillators and investigated the realization of associative memory. The phase difference of oscillator waveforms and the difference of output patterns by disconnecting the coupling between oscillators was clarified. As a result, it was confirmed that the realization of associative memory is possible using sparse coupling, however the maximum rate of coupling disconnection for which associative memory is possible depends on the combination of oscillators in the coupling system.

In this study, we propose the coupled oscillatory networks with the memristor couplings as a ring structure. We focus on the dynamics of the memristor couplings, and investigate synchronization phenomena. As a result, this study observes three different synchronization state types: multi-phase synchronization state, in-phase and anti-phase synchronization state and amplitude death. In addition, we analyze the power consumption of the memristor couplings to make clear the cause of phase sift.

We investigate neural activity of the neural network including delayed couplings by using attractor reconstruction images. The paper presents the effects on neural activity when the value and distribution of delayed coupling is changed. In particular, we focus on the complexity of attractor images and evaluate the relationship between delayed coupling and complexity.

This paper presents an investigation into networked reservoir computing using interconnected oscillators. Specifically, the Bonhoeffer-van der Pol (BVP) oscillator is used as the basis for the reservoir, and its performance is evaluated in speech recognition tasks. The results reveal that the BVP reservoir exhibits enhanced fidelity to input waveforms and improved accuracy in speech recognition compared to the van der Pol (VDP) reservoir. Furthermore, the impact of reservoir configuration on accuracy is examined by varying the number of oscillators, the connection probability, and the coupling strength. The results indicate that accuracy improves as the parameters are increased. These results emphasize the importance of carefully selecting and setting the dynamics of the physical system and parameters of the reservoir to achieve optimal performance.

If clustering can be performed in a continuous system, there is potential usefulness in processing speed for larger scale clustering. We have proposed clustering method of coupled chaotic circuit networks with learning. In this study, we propose a method to extract the center of a cluster by stabilizing synchronization through learning in networks where synchronization is not stable due to chaos.

In this study, we focus on an effect of frustration to triangular oscillatory network with stochastically coupling. We propose a coupled nonliear circuit network with stochastically coupling. Frustration as environmental factor is occurred by network topology which is composed from polygonal structure. We investigate synchronization of the proposed network using different frustration levels by changing the coupling strength. By using computer simulations, the effect of frustration to triangular oscillatory networks with stochastically coupling is shown.

In this study, we investigate synchronization phenomena in ladder-coupled chaotic circuits including ring structures. We focus on the effect of the position of the ring structures in the network to the synchronization of the whole network. In our proposed network model, chaotic circuits are coupled by resistors. We set the parameters of the circuits to generate periodic solutions or chaotic solutions. Computer simulations confirm that the various synchronization phenomena occur in different situations.

In this study, we investigate amplitude change and amplitude death in coupled van der Pol oscillators with different oscillation frequency. The network topology is inspired by the concept of a real brain network. We observe the amplitude death of all oscillators when the coupling strength reaches to the certain value. We also confirm that the characteristics of amplitude death depends on the position of oscillator with different oscillation frequency.

In this study, we focus on network structure with a hub of Scale-free network. We consider the synchronization of coupled chaotic circuit network whose hub nodes are split two nodes and we investigate two network models. In one of them, we replace a hub node with two distributed nodes connected by a positive resistor. In another, we replace a hub node with two distributed nodes connected by a negative resistor. From the simulation results, we can confirm that connection between hubs plays an important role in network structure.

In this study, we propose a method of generating complex networks by exploiting synchronization between coupled oscillatory circuits. To each node of a 2D fully connected network a van der Pol oscillator is assigned. We then study the topological evolution of the network in dependence on environmental conditions. These conditions are modeled by considering the distance between the oscillators and some small frequency errors that are added. By carrying out computer simulations, we confirm that different types of complex networks are obtained depending on different environmental conditions.

In this study, synchronization observed in dynamical oscillatory networks with non-uniform coupling distributions is investigated. The coupling states (on/off) of all connections are stochastically determined at every certain time with the coupling probability. We focus on the heavy tail type of coupling distribution for the network. By using computer simulations, we confirm that the dynamical network with the heavy tail type of coupling distribution can hardly achieve global synchronization.

A brain has neuron and glial cells. In the brain, these cells correlate each other and make a higher brain function. In this study, we propose a Multi-Layer Perceptron (MLP) with pulse glial chain having oscillatory excitation threshold. We connect artificial glia units with the neurons in a hidden-layer. When the connecting neuron output is larger than an excitation threshold of the connected glia, the glia excites and generates the pulse. This pulse transmits to the neighboring glias and the connecting neuron. The pulse increases a threshold of the connecting neuron, thus the glia gives energy for solving tasks. In this model, the excitation threshold is oscillating within a defined value. Even if the connecting neuron output does not change, the pulse generation occurs by the oscillation of the excitation threshold. The oscillation of the excitation threshold gives more energy to the network and improves a learning performance of the MLP. By computer simulation, we confirm that the oscillation of the excitation threshold improves a learning performance.

In this study, we investigate synchronization of parametrically excited van der Pol oscillators with small mismatch. In the case of two subcircuits, we confirm that the two subcircuits are synchronized at in-phase state when the adding mismatch is small. By increasing the small mismatch, we observe unsynchronous phenomena. Furthermore, we apply this circuit model to ten coupled oscillators as random network model with hub.

In this study, we propose a Multi-Layer Perceptron (MLP) with pulse glial chain based on individual inactivity period which is inspired from biological characteristics of a glia. In this method, we one-by-one connect a glia with neurons in the hidden-layer. The connected glia is excited by the connecting neuron output. Then, the glia generates the pulse. This pulse is input to the connecting neuron threshold. Moreover, this pulse is propagated into the glia network. Thus, the glia has a position density each other. In this network, a period of inactivity of the glia is dynamically changed according to pulse generation time. In the previous method, we fix the period of inactivity, thus the pulse generation pattern is often fixed. It is similar to the local minimum. By varied the period of inactivity, the pulse generation pattern obtains the diversity. We consider that this diversity of the pulse generation pattern is efficiency to the MLP performance. By the simulation, we confirm that the proposed MLP improves the MLP performance than the conventional MLP.

Epilepsy of the neuropsychiatric disorder is provoked from an imbalance in the long-term potentiation (LTP) versus long-term depression (LTD) of the synapses in the hippocampus. The LTP and LTD are replicated by using the Spike Timing Dependent Plasticity (STDP). Additionally, the spiking activity of the synapses in the hippocampus can be approximated by using the Rulkov maps. In our previous study, we considered some easy simulation models which are constructed by using Rulkov maps with STDP. Moreover, these simulation models consist of unidirectionally coupled neurons. In this paper, we consider some easy simulation models with bidirectionally coupled neurons. We explore the effect of unidirectional and bidirectional connection on spiking activity, as basic simulation for constructing the approximate simulation model of epilepsy. From these results, the unidirectional models show high accuracy in-phase/anti-phase synchronization, and it shows divergent relatively early. The bidirectional models show the stable waveform (i.e., non-divergent) for a long term compared to unidirectional models.

Chaos synchronization can be observed in various natural systems. In this study, we investigate synchronization phenomena on a ring of coupled chaotic circuits. We can observe N-phase chaos synchronization and simple synchronization phenomena depending on initial values in the system. This paper suggests ring topology and alternately shifted bias are essential factors of N-phase synchronization.

We have recently proposed a novel neural network structure called an “Affordable Neural Network” (AfNN), in which affordable neurons of the hidden layer are considered as the elements responsible for the robustness property as is observed in human brain function. In this study, we investigate the mechanism of learning process of the AfNN to make clear the reason of that the AfNNs can perform well for learning and generalization abilities and operate as usually against damaging neurons.

A glia is a nervous cell existing in a brain. This cell transmits signals by various ion concentrations. By the ion concentration, the glia correlates the neuron by ions. Thus, the glia composes the network different to the neural network. We proposed a four-layer Multi-Layer Perceptron (MLP) with glia connections of hidden-layer neurons which is inspired from characteristics of the biological glia. The proposed MLP has two hidden-layer. In this network, we connect the glia to the neurons in two hidden-layer. The glia receives the output of connecting neuron and the outputs are summed. When the summed value is over the glia excitation threshold, this glia is excited. The excited glia generates a pulse. This pulse input to the thresholds of connecting glias. We consider that the glia gives the energy to the MLP and the glia give the position relationship of neurons in the different layers. By the simulation, we confirm that the proposed MLP obtains a high solving ability. Moreover, we investigate the characteristics of the proposed MLP.

In this study, we research a new layer arrangement of three layer cellular neural network (CNN). In this paper, we investigate the output characteristics by using our proposed method to image processing of gray scale image and binary image and show its effectiveness with simulation results.

A glia is a nervous cell which is existing in a brain. This cell changes a Ca2+concentration. This ion affects a neuron membrane potential and it is propagated to the neighboring glia. Moreover, the Ca2; directly affects the human memory by increasing of a D-serine. From these functions, we propose a Multi-Layer Perceptron (MLP) including glial pulse and switching between a learning and non-learning. In this method, the neurons in the hidden-layer received the pulse from connected glias. The pulse is generated depending on the neuron outputs and it is propagated to the neighboring glias and neurons. Moreover, the neurons are separated to some groups. Each group periodically switches a learning term and a non-learning term. Each group starts the learning term having a small lag each other. We consider that a performance of the MLP improves by two different methods influencing each other. By two simulations, we confirm that the MLP obtains the high solving ability by using our methods.

Genetic Algorithm (GA) is modeling behavior of evolution in organic and known as one of method to solve Traveling Salesman Problem (TSP). However, GA obtains solution by overlaying generations because of being based on evolution in organic. Thus, it takes a long time to find approximate solution. While, Virus Theory of Evolution (VTE) can evolve by virus infection. VTE characteristic has sharing of information among same generation. If new algorithm is using both these characteristics of GA and VTE, convergence speed would be faster than GA. Thus, this study proposes Genetic Algorithm with Virus Infection (GAVI). GAVI algorithm is Virus Theory of Evolution (VTE) to be based on Genetic Algorithm (GA). We apply GAVI to TSP and confirm that GAVI obtains more effective result than GA.

In this study, synchronization phenomena observed in coupled polygonal oscillatory networks with frustration is investigated. We focus on the amplitude of each oscillator when the coupling strength is changed. By using computer simulations and circuit experiments, we confirm that the amplitude of the shared and the other oscillators obtains different value by increasing the value of the coupling strength. Furthermore, theoretical analysis is applied to solve the amplitude and the phase differences between the adjacent oscillators.

A glia is a nervous cell existing in a brain. The brain is composed of the relationship with glias and neurons. By an ion concentration, the glia transmits signal to neurons and neighboring glias. In this study, we propose the MLP with positive and negative pulse glial chain which is inspired from features of the biological glia.We add the MLP to the positive and negative pulse glial chain. In the positive and negative pulse glial chain, the glias are connected to the neurons one by one. The glia generates pulse when the glia is excited by the connected neuron's output. If the connected neuron has large amount of output, the glia generates positive pulse. Moreover, if the connected neuron has small amount of output, the glia generates the negative pulse. The positive and negative pulse are propagated to the connected neuron and neighboring glias. We consider that the positive and negative pulse glial chain give the relationships of position of neurons in a same layer. By solving a Two-Spirals Problem (TSP), we confirm that the proposed MLP has better a learning performance and a generalization capability than the conventional MLP.

Solving combinatorial optimization problems is one of the important applications of neural networks. Many researchers have proposed noise induced hopfield neural networks in which noises are induced state values of neurons. However, the noise inducing method to state values of neurons cause problems. In this study, we propose hopfield neural networks with periodic brake. In the proposed system, external noises are not induced to state values of neurons. Thus, the proposed system can avoid the problem caused in the noise induced system. We investigate the solving ability of the proposed system for quadratic assignment problems and designing of parameters.

A glia is nervous cell which exists in a brain. The glia can transmit signal to other glias and neurons by change of ions' densities. We have an interest in this feature of the glia. We consider that we can apply this feature to an artificial neural network. In this study, we propose a Multi-Layer Perceptron (MLP) with propagation of glial pulse to two directions. The proposed MLP has the glias in a hidden layer. The glias are connected with neurons and are excited by the outputs of neurons. The exciting glias generate pulses and the pulses affect neurons' thresholds and neighboring glias. We consider that the MLP obtains the relationships of position of neurons in the hidden layer and this information give good influence to the MLP leaning. We confirm that the proposed MLP has better learning performance than the conventional MLP. Moreover, we confirm that the performance of the proposed MLP is changed by some conditions of propagation of the glial pulse.

In this study, we analyze a synchronization phenomenon observed from a circuit network consisting of several number of oscillator chains which are one-dimensional arrays of weakly coupled van der Pol oscillators. Computer simulations and circuit experiments show interesting unexpected synchronization phenomenon and theoretical analysis explains the synchronization state.

In this study, we investigate synchronization phenomena in coupled chaotic circuits which are connected with distance information. We confirm that the chaotic circuits arranged in the near distance are synchronized at in-phase state, and the coupled circuits with the far distance could not be synchronized. Namely, clustering phenomena of coupled chaotic circuits is observed on two-dimensional place.

Honeybee Colony Optimization (HCO) is an optimization algorithm based on a particular intelligent behavior of honeybee swarms. In this study, we propose a new HCO containing a characteristic of guidepost pheromone that has the effect to attract other bees. Namely, many bees can move to the optimal place. We investigate the performance of the proposed HCO by using four benchmark problems. It discovered that the effect of guidepost pheromone works well for the high dimension problems. We consider that the proposed HCO with pheromone can leave from local minima more easily than the standard HCO.

We have proposed the glial network which was inspired from the feature of the brain. The glial network is composed by glias connecting each other. All glias generate oscillations and these oscillations propagate in the glial network. We confirmed that the glial network improved the learning performance of the Multi-Layer Perceptron (MLP). In this article, we investigate the MLP with the impulse glial network. The glias generate only impulse output, however they make the complex output by correlating with each other. We research the proposed networks' parameter dependency. Moreover, we show that the proposed network possess better learning performance and better generalization capability than the conventional MLPs.

In this study, we propose Cellular Neural Networks with switching two types of templates. In the CNN, space varying system is known that it can perform complex processing. Generally, the space varying CNN is not easy to design. However, we can set existing template on each cell of CNN by the proposed method. In binarization, complex portions of input image are not processed well by using the conventional CNN. On the other hand, the complex portion can be processed well by the proposed method. In the edge detection, the indistinct portion is not detected by the conventional CNN with “Edge detection” template of 3×3 matrix. It is difficult for CNN to recognize that it is the edge or not. Additionally, the detected edge is too bold and some noises are left with “Edge detection” template of 5×5 matrix. By switching these templates in case, we can detect edge in indistinct position. In pattern formation, generally, simple pattern is formed by using one template. On the other hand, some complex patterns are formed by the proposed method. From some simulation results, we confirm that the proposed method is effective for various image processing.

In our previous study, we have proposed an Ant Colony Optimization with Intelligent and Dull Ants (IDACO) which contains two kinds of ants. We have applied IDACO to various Traveling Salesman Problems (TSPs) and confirmed its effectiveness. This study proposes an Ant Colony Optimization Changing the Rate of Dull Ants (IDACO-CR) and its Application to Quadratic Assignment Problems (QAPs). In addition to the existence of the dull ants which cannot trail the pheromone, the rate of dull ants in IDACO-CR is changed flexibly and automatically in the simulation, depending on the problem. We investigate the behavior of IDACO-CR in detail and the effect of changing the rate of dull ants. Simulation results show that IDACO-CR gets out from the local optima by changing the rate of dull ants, and we confirm that IDACO-CR obtains the effective results in solving complex optimization problems.

Two coupled logistic maps whose parameters are forced into periodic varying are investigated. From the investigation of bifurcation in this system, nonexistence of odd periodic orbit except fixed point and existence of many coexisting attractors, which consist of periodic orbits or chaotic orbits, are observed. Basins where boundary depends on the invariant manifold of saddle points are numerically analyzed by considering second order iteration and using superposition with Newton method, although the system has discontinuity.

In this study, synchronization phenomena observed in coupled polygonal oscillatory networks with frustration is investigated. We focus on the power consumption of coupling resistors of the whole system. By using computer simulations and theoretical analysis, we confirm that the phase differences of the coupled oscillators are solved by finding the minimum value of the power consumption function.

This study proposes a Batch-Learning Self-Organizing Map with Weighted Connections avoiding false-neighbor effects (BL-WCSOM). We apply BL-WCSOM to several high-dimensional datasets. From results measured in terms of the quantization error, inactive neurons, the topographic error and the computation time, we confirm that BL-WCSOM obtain the effective map reflecting the distribution state of the input data using fewer neurons in less time.

In this study, we propose a space-varying cellular neural network (CNN) designed by Hopfield neural network (Hopfield NN). CNN is classified into two types of system like space-invariant system and space-varying system. Space-invariant means that all cells have identical template. On the other hand, space-varying means that all cells do not have identical template according to the state values of the cell and neighbor cells and so on. The proposed CNN is the space-varying system and it is designed by using an associative memory ability of Hopfield NN. In general, the design of space-varying systems is not easy. However, we can set one of prepared existing templates on each cell of CNN according to the retrieved pattern by Hopfield NN to which some typical local image structures are embedded. Namely, we need only some existing templates and their associated patterns. Some simulation results show the basic properties of the proposed CNN and its effectiveness.

We have recently proposed a novel neural network structure called an “Affordable Neural Network” (AfNN), in which affordable neurons of the hidden layer are considered as the elements responsible for the robustness property as is observed in human brain function. We have confirmed that the AfNN gains good performance both of the generalization ability and the learning ability. Furthermore, the AfNN has durability, because the AfNN still performs well even if some of neurons in the hidden layer are damaged after learning process. In this study, we study the characteristics of weights of the AfNN during the learning process to make clear the reason of that the AfNNs can perform well for learning and generalization abilities and operate as usually against damaging neurons.

This paper considers a novel error-correcting scheme exploiting chaotic dynamics for noncoherent chaos communication. In our proposed system, two successive chaotic sequences are generated from the same chaotic map; the second sequence is generated with an initial value which is the last value of the first sequence. In this case, successive chaotic sequences having the same chaotic dynamics are created. This feature gives the receiver additional information to correctly recover the information data and thus improves the bit error performance of the receiver. Further, enhanced efficiency also comes from operating on successively modulated data; by involving less redundancy in the error correction system, it can be designed with high coding rate. In this paper, we analyze the scheme's capability, by examining computational times and accuracy rates of error correction, bounds on its capability.

Some methods using artificial neural network were proposed for solving to the Two-Spiral Problem (TSP). TSP is a problem which classifies two spirals drawn on the plane, and it is famous as the high nonlinear problem. In this paper, we propose a chaos glial network which connected to Multi-Layer Perceptron (MLP). The chaos glial network is inspired by astrocyte which is glial cell in the brain. By computer simulations for solving TSP, we confirmed that the proposed chaos glial network connected to MLP gains better performance than the conventional MLP.

Synchronization phenomena in coupled logistic maps whose parameters are forced into periodic varying are investigated through the use of Lyapunov exponents. When three maps are coupled, various synchronization phenomena are observed by choosing a coupling intensity. The synchronization phenomena fall into three general categories, which are asynchronous, synchronization of two among the three maps and synchronization of all the maps. In particular, in the synchronization of two of the three maps, solutions of maps behave periodic, quasi-periodic and chaotic for several coupling intensities.

This study proposes the Self-Organizing Map with Weighted Connections avoiding false-neighbor effects (WC-SOM). We investigate the effectiveness of WC-SOM in comparison with the conventional SOM, Growing Grid and FN-SOM. We confirm that WC-SOM enables the most flexible self-organization among the four algorithms and can obtain the effective map reflecting the distribution state of the input data using fewer neurons.

A novel performance driven facial shape animation method is presented for mapping the expressions from the source face to the target face automatically. Unlike the prior expression cloning approaches, the proposed method aims to animate a new target face with the help of real facial expression samples. The basic idea is to learn the shape deformation from samples for target face to generate corresponding expressions. The process consists of two main stages. First of all, source motion vectors are transferred by statistic face model to generate a reasonable expression on the target face. And then, local deformation constraints are proposed to refine the animation results. In the second part, the local deformation characters for each target organ are learned from the samples, which preserve the personality as well as the expression styles. Experimental results on different facial animation demonstrate the feasibility and effectiveness of the proposed method.

The self-organizing map (SOM) is a famous algorithm for the unsupervised learning and visualization introduced by Teuvo Kohonen. One of the most attractive applications of SOM is clustering and several algorithms for various kinds of clustering problems have been reported and investigated. This study proposes the community self-organizing map (CSOM) algorithm which reflects the community in the human society. In CSOM algorithm, the neurons create some communities according to their winning frequency. We apply CSOM to various input data for clustering and data extraction, and we investigate its behaviors. We confirm that CSOM creates some communities and obtain efficient results for data extraction.

In this study, we propose a three-layer cellular neural network dealing with color images considering the HSB model. Firstly, the conversion method of color images to three grayscale images using the HSB model is explained. Secondly, the structure of the proposed three-layer cellular neural network considering the HSB model is explained. Simulation results of some color image processing show the basic properties of the proposed network and its effectiveness.

In this research, we propose cellular neural network with dynamic template. In our proposed cellular neural network, the template of cell is changed at each update by learning. The learning method considering rank order learning is inspired from self-organizing map. Some computer simulations using the proposed cellular neural network with dynamic template are carried out and its output characteristic is investigated through simple image processing task.

This study proposes a new network-structured particle swarm optimizer considering neighborhood relationships (NS-PSO). All particles of NS-PSO are connected to adjacent particles by a neighborhood relation of the 2-dimensional network. The directly connected particles share the information of their own best position. Each particle is updated depending on the neighborhood distance on the network between it and a winner, whose function value is best among all particles. Simulation results show the searching efficiency of NS-PSO.

This paper proposes the error-correcting scheme without redundancy sequences based on the chaotic dynamics for noncoherent chaos communications. We generate successive chaotic sequences from the identical chaotic map. And for the next sequence we set the initial value to the end value of the former sequence. By such way we can create the successive chaotic sequences having the same chaotic dynamics. This feature gives the receiver additional information to correctly recover the received noisy signal. Therefore, by analyzing the chaotic dynamics at the receiver, it is possible to improve the error performance without redundancy signal. As results of computer simulations, we confirm about 3 dB gain in BER performance as compared with the conventional suboptimal receiver when using the short chaotic sequence length per 1 bit.

In this study, we investigate synchronization states of small oscillations observed from simple two chaotic circuits cross-coupled by inductors by both computer simulations and circuit experiments. We confirm that there are many different synchronization states coexist.

We explore the behavior of two van der Pol oscillators coupled by a stochastically time-varying resistor. We observe switching between in-phase and anti-phase synchronization and analyze the statistics of the switching, with and without additional noise. We find stochastic resonant behavior of synchronization in both regimes. Correlating the two noise sources quickly destroys the stochastic resonance phenomenon in the anti-phase synchronization regime. In the in-phase regime, noise correlation first contributes to the synchronization before removing the resonance by taking over the synchronization by its own means.

The Self-Organizing Map (SOM) is a famous algorithm for the unsupervised learning and visualization introduced by Teuvo Kohonen. This study proposes the Lazy Self-Organizing Map (LSOM) algorithm which reflects the world of worker ants. In LSOM, three kinds of neurons exist: worker neurons, lazy neurons and indecisive neurons. We apply LSOM to various input data set and confirm that LSOM can obtain a more effective map reflecting the distribution state of the input data than the conventional SOM.

In this research, we propose cellular neural networks using mixture template as an example of space-varying cellular neural networks. As the first step of the investigation of such complex nonlinear circuit networks, we propose two mixing methods of the templates and investigate the output characteristics of the simple image processing with a binary image and a grayscale image by computer simulations.

Adaptive Resonance Theory (ART) is an unsupervised neural network. Fuzzy ART (FART) is a variation of ART, allows both binary and continuous input patterns. However, Fuzzy ART has the category proliferation problem. In this study, to solve this problem, we propose a new Fuzzy ART algorithm: Fuzzy ART Combining Overlapped Category in consideration of connections (C-FART). C-FART has two important features. One is to make connections between similar categories. The other is to combine overlapping categories into with connections one category. We investigate the behavior of C-FART, and compare C-FART with the conventional FART.

This study proposes a Batch-Learning Self-Organizing Map with False-Neighbor degree between neurons (called BL-FNSOM). False-neighbor degrees are allocated between adjacent rows and adjacent columns of BL-FNSOM. The initial values of all of the false-neighbor degrees are set to zero, however, they are increased with learning, and the false-neighbor degrees act as a burden of the distance between map nodes when the weight vectors of neurons are updated. BL-FNSOM changes the neighborhood relationship more flexibly according to the situation and the shape of data although using batch learning. We apply BL-FNSOM to some input data and confirm that FN-SOM can obtain a more effective map reflecting the distribution state of input data than the conventional Batch-Learning SOM.

Studies on chaos synchronization in coupled chaotic circuits are extensively carried out in various fields. In this study, synchronization patterns generated in a ring of cross-coupled chaotic circuits are investigated. Computer simulations show that this coupled system produces several phase patterns.

This study improves SOM with False-Neighbor degree between neurons (FN-SOM) which solves some problems of the original FN-SOM. The improved FN-SOM changes the neighborhood relationship more flexibly according to the situation and the shape of data. We investigate the behavior of FN-SOM in detail. Learning performance is evaluated both visually and quantitatively using the three measurements. We confirm effectively of the improved FN-SOM with comparing with the conventional SOM, Growing Grid and the original FN-SOM.

One of the applications of neural network is solving combinatorial optimization problems. In our past study, the solving ability of the Hopfield Neural Network with noise for quadratic assignment problem is investigated. However, even if we injected the noise to the network, the optimal solution cannot occasionally be found. In this study, we propose the method adding scale-rule noise to the Hopfield Neural Network to achieve better performance. By computer simulations solving quadratic assignment problem, we evaluate the performance of the method.

Studies on chaos synchronization in coupled chaotic circuits are extensively carried out in various fields. In this study, two simple chaotic circuits cross-coupled by inductors are investigated. Interesting state transition phenomenon around chaos synchronization is observed by computer simulations and circuit experiments.

Synchronization phenomena in coupled oscillatory systems are very important model to describe various higher-dimensional nonlinear phenomena in the field of natural science. In this study, we investigate synchronization phenomena in van der Pol oscillators coupled by time-varying resistors with timing mismatch as a ring. By carrying out computer simulations, we confirm the various interesting phenomena (wave propagation, clustering, complex phase pattern) which cannot be observed in simple oscillatory systems coupled by resistors.

This paper proposes a new noncoherent detection system improved based on the differential chaos shift keying (DCSK) and the correlation delay shift keying (CDSK). In this scheme, a transmitter changes chaotic maps for generating a chaotic sequence efficiently depending on an initial value. Also, the proposed method can choose the chaotic map by a very simple algorithm. In order to investigate the proposed method, computer simulations were carried out and observe the performance.

In the real world, it is not always true that the next-door house is close to my house, in other words, "neighbors" are not always "true neighbors". In this study, we propose a new self-organizing map (SOM) algorithm which considers the false neighboring neuron (called FNN-SOM). The FNN-SOM self-organizes with considering the real neighboring relation. The behavior of FNN-SOM is investigated with learning for various input data. We confirm that we can obtain the more effective map reflecting the distribution state of input data than the conventional SOM.

Many people propose the method adding chaos noise to Hopfield neural network for solving combinatorial optimization problems. In our past study, we solved quadratic assignment problem by Hopfield neural network with various chaotic noises. However, the solution of the network is sometimes trapped in a certain area and can not find a good solution, especially for relatively larger problems. In this study, we propose a method changing the amplitude of the chaos noise. We investigate the performance of Hopfield neural network with the hopping chaos for quadratic assignment problem.

In this study, two chaotic circuits coupled by a time-varying resistor are investigated. We assume that the time-varying resistor is realized by switching positive and negative resistors alternately. By carrying out circuit experiments and computer simulations, we confirm that interesting switching phase states between in-phase and anti-phase is observed.

Since we can accumulate a huge amount of data including useless information in these years, it is important to investigate various extraction method of clusters from data including a lot of noises. The self-organizing map (SOM) attracts attentions for clustering in these years. In our past study, we have proposed a method of using simultaneously two kinds of SOMs whose features are different (nSOM), namely, one self-organizes the area on which input data are concentrated, and the other self-organizes the whole of the input space. Further, we have applied this method to clustering of data including a lot of noises and have confirmed the efficiency. However, in order to obtain an efficient clustering performance using this method, we must determine the appropriate number of the SOMs used in the method. This problem has been remedied by proposing the Peace SOM (PSOM) method, however, PSOM algorithm must be used after executing the nSOM method. In this study, we propose a method of using plural SOMs (TSOM: Tentacled SOM) for effective data extraction, which possesses both abilities of nSOM and PSOM. Each SOM of TSOM can catch the information of other SOMs existing in its neighborhood and self-organizes with the competing and accommodating behaviors. The behavior of TSOM is investigated with applications to data extraction from input data including a lot of noises. We can confirm that TSOM successfully extracts clusters even in the case that we do not know the number of clusters in advance.

The Self-Organizing Map (SOM) is popular algorithm for unsupervised learning and visualization introduced by Teuvo Kohonen. One of the most attractive applications of SOM is clustering and several algorithms for various kinds of clustering problems have been reported and investigated. In this study, we propose a new type of SOM algorithm, which is called Fatigable SOM (FSOM) algorithm. The important feature of FSOM is that the neurons are fatigable, namely, the neurons which have become a winner can not become a winner during a certain period of time. Because of this feature, FSOM tends to self-organize only in the area where input data are concentrated. We investigate the behavior of FSOM and apply FSOM to clustering problems. Further, we introduce the fatigue level to FSOM to increase its flexibility for various kinds of clustering problems. The efficiencies of FSOM and the fatigue level are confirmed by several simulation results.

In recent years, many people have been trying to develop some applications to information processing by exploiting oscillatory phenomena in neural networks. Bifurcation and stability of equilibrium points in a simple neural oscillator consisting of two neurons have been analyzed in detail. On the other hand, oscillatory phenomena in the simple neural oscillator can be modeled by electrical circuit such as van der Pol oscillators. In this study, we propose a network model of simple oscillators coupled by time-varying resistor, which can explain some interesting complex phenomena observed in a large scale network of neurons coupled by both excitability and inhibitory synapses. By carrying out computer simulations and circuit experiments, we confirm the generation of various interesting phenomena which cannot be observed in simple coupled oscillatory networks.

In this study, we address the durability of the brain, which is able to operate in various imperfect situations. In our previous research, we have proposed a new network structure called the "Affordable Neural Network", where affordable neurons in the hidden layer of the feedforward neural network reflect important aspects of the real brain mechanism. We consider that the concept of the affordable neural network embodies the important feature of durability. In our contribution, we investigate the durability of the affordable neural network when some of the neurons in the hidden layer are damaged after the learning process.

The self-organizing map (SOM) attracts attentions for clustering in these years. In our past study, we have proposed a method using simultaneously two kinds of SOMs whose features are different, namely, one self-organizes the area on which input data are concentrated, and the other self-organizes the whole of the input space. Further, we have applied this method to clustering of data including a lot of noises and have confirmed the efficiency. However, in order to obtain an efficient clustering performance using this method, we must determine the appropriate number of the SOMs used in the method. In this study, we propose the peace SOM (PSOM) algorithm which possesses both competing and accommodating abilities. The competing and the accommodating behaviors of PSOM are investigated with applications to clustering input data including a lot of noises. We can see that PSOM successfully extracts clusters even in the case that we do not know the number of clusters in advance

There are many kinds of optimization techniques for designing high-performance RF circuits. In this paper, we propose a new frequency-domain Spice-oriented optimization algorithm using the steepest descent method. We have developed a simulator executing the frequency-domain analysis based on the harmonic balance (HB) method, where all the nonlinear devices such bipolar transistors and MOSFETs are replaced by the equivalent HB modules. The objective functions in the optimization are estimated by the DC analysis of the modified HB circuits. On the other hand, our steepest descent algorithm is realized by equivalent circuit model. Thus, the optimum solution is stably found by the transient analysis of Spice. We show the formulation of HB circuits in section II, the Spice-oriented optimization algorithm in section III and the interesting examples in section IV

Solving combinatorial optimization problems is one of the important applications of the neural network. Many researchers have reported that exploiting chaos achieves good solving ability. However, the reason of the good effect of chaos has not been clarified yet. In this study, we investigate a performance of chaotic switching noise injected to the Hopfield neural network for quadratic assignment problems. By computer simulation we confirm that the chaotic switching noise is effective for solving quadratic assignment problems as well as intermittent chaos near three-periodic window

A fast timing analysis of plane circuits via two-layer CNN-based modeling, which is necessary for the solution of power/signal integrity problems in printed circuit boards and packages, is presented. Using the new notation expressed by the two-layer CNN, more than 1500 times faster simulation is achieved, compared with Berkeley SPICE (ngspice). In CNN community, CNNs are generally simulated by explicit numerical integration algorithms such as the forward Euler and Runge-Kutta methods. However, since the system of the two-layer CNN becomes stiff, we cannot analyze the CNN by using an explicit numerical integration algorithm. Hence, to analyze the two-layer CNN and reduce the computational cost, the leapfrog method is introduced in this paper. This procedure would open a CNN application up to electronic design automation area

Several people point out that the Hopfield neural network (abbr.NN ) with chaos injection gains the good performance for solving traveling salesman problems, which is one of combinatorial optimization problems. In this study, we investigate the performance of the intermittency chaos injected to the Hopfield NN working as an associative memory. The rate and the speed of the convergence to an embedded pattern are evaluated by computer simulations.. Furthermore, in order to confirm the reason of the good ability of intermittency chaos, we carry out the same simulation using the time series produced by the Markov chain model. Simulated results show that the Markov chain model is good enough to gain similar performance of the intermittency chaos.

It is a very important, but difficult, task to calculate the multiple dc solutions in circuit simulations. In this paper, we show a very simple SFICE-oriented Newton homotopy method which can efficiently find out the multiple de solutions. In the paper, we show our solution curve-tracing algorithm based on the arc-length method and the Newton homotopy method. We will also prove an important theorem about how many variables should be chosen to implement our algorithm. It verifies that our simulator can be efficiently applied even if the circuit scales are relatively large. In Section III, we show that our Newton homotopy method is implemented by the transient analysis of SPICE. Thus, we do not need to formulate a troublesome circuit equation or the Jacobian matrix. Finally, applying our method to solve many important benchmark problems, all the solutions for the transistor circuits could be found on each homotopy path. Thus, our simulator can be efficiently applied to calculate the multiple dc solutions and perhaps all the solutions.

In this study, synchronization phenomena in chaotic oscillators coupled by a transmission line are investigated. In particular investigation using real circuits is done for the first time. We report the very interesting results.

In this study, the phenomena related to collisions between two phase-version-waves in an array of van der Pol oscillators are investigated. Behavior of the two phase-inversion-waves after they collide with each other is classified by computer simulations. Further, the mechanisms of the complete extinction and reflection of the two waves by using the relationship between phase states and oscillation frequencies are explained.

There are many communication circuits driven by multitone signals such as modulators and mixers, and so on. In this case, if frequency components of the modulators are largely different, the brute force numerical integration will take an enormous computation time to get the steady-state responses, because the step size must be chosen depending on the highest frequency input. The same situation happens to mixer circuits which generate very low frequency output. In this paper, an efficient algorithm is shown to solve the communication circuits driven by multitone signals which is based on the frequency-domain relaxation method and the multi-dimensional Fourier transformation. Attenuation of the transient phenomena mainly depends on the reactive elements such as capacitors and inductors, so that we partition the circuit into two groups of the nonlinear resistive subnetworks and the reactive elements using the substitution sources. The steady-state response can he calculated in such a manner that the responses at each partitioning point have the same waveform. We have developed a simple simulator carrying out our algorithm that only uses the transient, dc-analysis and ac-analysis of SPICE. It can be easily applied to relatively large scale integrated circuits, efficiently, We found from many simulation results that the convergence ratio at the iteration of our relaxation method is sufficiently large, and can be applied to wide class of the communication circuits.

In this study, four simple autonomous chaotic circuits coupled by one resistor are investigated. By carrying out computer calculations and circuit experiments, it is shown that our very simple coupled circuit can exhibit spatiotemporal chaos as well as quasi-synchronizations of chaos in spite of the fact that the number of chaotic subcircuits is only four.

There have been many investigations of the mutual synchronization of oscillators. In this study we consider N oscillators which have the same natural frequency, mutually coupled by one negative resistor. In this system, according to the negative range of the coupling negative resistor, we can observe various interesting synchronization phenomena, because this system tends to minimize the power consumption in the coupling negative resistor. Especially when the I-V characteristics of the coupling negative resistor are not third-power, but linear and negative, this system tends to be stable by producing the power in the coupling one. The phase states are changed according to the negative range of the coupling negative resistor.>

There have been many investigations of the mutual synchronization of oscillators. Especially, we have analyzed N van der Pol type LC oscillators coupled by one resistor, and confirmed that this system can take a huge number of steady states. In this study, we analyze N Wien-bridge oscillators with the same natural frequency mutually coupled by one resister by both of computer calculations and circuit experiments. Because there is no inductor in this system, it is suitable for VLSI implementation. Moreover, the system has many more phase states than that of the system with van der Pol type LC oscillators when N is not so large. So we can utilize this system as a structural element of cellular neural networks.>

In this study, multimode chaos observed from two coupled chaotic oscillators with hard nonlinearities is investigated. At first, a simple chaotic oscillator with hard nonlinearities is realized. It is confirmed that in this chaotic oscillator the origin is always asymptotically stable and that the solution, which is excited by giving relatively large initial conditions, undergoes period-doubling bifurcations and bifurcates to chaos. Next, four different modes of oscillations are observed from two coupled chaotic oscillators with hard nonlinearities by both computer calculations and circuit experiments. One of the modes of oscillation is a nonresonant double-mode oscillation and this oscillation is stably generated even in the case that oscillation is chaotic. Namely, for this oscillation mode, chaotic oscillation and periodic oscillation can be simultaneously excited. We call this phenomena double-mode chaos. Finally, the beat frequency of the double-mode chaos is confirmed to be changed by tuning the value of the coupling capacitor.>

In this study, simultaneous asynchronous oscillations of chaos observed in a simple autonomous circuit are investigated by both of circuit experiments and computer calculations. This circuit consists of two nonlinear subcircuits having different oscillation frequencies coupled by one linear negative resistor. In the case that oscillation frequencies of two subcircuits are quite different, both subcircuits exhibit asynchronous chaos simultaneously. Namely, the oscillation is almost divided into two different kinds of chaos, one includes many lower frequency components and the other one includes many higher frequency components. We call this interesting phenomenon as twin chaos. Moreover, as the different between oscillation frequencies of two subcircuits decreases, two difference kinds of chaos interfere mutually. Finally, twin chaos disappears and chaos which is the same as the case of one subcircuit appears due to mutual leading-in.>