In this work, black-box modeling techniques are applied to Bacterial Cellulose-based sensors to characterize their dynamic behavior. Several classes of linear and nonlinear models, including Finite Impulse Response, AutoRegressive with eXogenous Input, Nonlinear Finite Impulse Response, Nonlinear AutoRegressive with eXogenous Input, and Long Short-Term Memory networks, are developed and compared. The performance of each model is evaluated based on a one-step-ahead prediction and a ∞-step-ahead simulation using standard performance metrics such as Root Mean Square Error, Mean Absolute Error and the coefficient of determination. The results show the strengths and limitations of the different modeling approaches in capturing the dynamics of BC-based transducers. The ARX model showed the best results for the prediction in one step, but poor results were obtained when the prediction was considered in ∞ steps. The NFIR model is instead the best choice for long-term prediction.

Bacterial Cellulose (BC) functionalized with Ionic Liquids (ILs) is a promising candidate for sustainable electroactive sensors. While single-carrier transport models are well-established in piezoionic electroactive polymers, their applicability to BC-IL systems remains unverified. This study introduces a simplified 1D finite element model, significantly improving computational efficiency while preserving key physical insights. A detailed parametric analysis investigates the impact of different charge transport assumptions, revealing that single-carrier models are insufficient to fully describe the mechanoelectric transduction behavior. The results emphasize the necessity of a dual-carrier framework to accurately model BC-based transducers, offering a deeper understanding of multi-ionic interactions within the porous BC structure. By highlighting key mechanisms and limitations, this work provides a foundation for optimizing BC-IL sensors, preparing the way for more reliable and scalable bioelectronic applications.

In this paper a dielectric structure made with Carbon Black has been designed to realize a Fractional Order Element (FOE). This device is hence used as capacitor to build a fractional-order Wien oscillator. FOE experimental frequency characterization, theoretical and experimental simulations on Wien oscillator are provided.

This paper provides an overview of the distributed parameter properties of non-Newtonian fluids and a proposal for identifying them. A low cost setup is described along with a proposed methodology protocol. The paper introduces the problem of moving from a linear framework of fluid properties towards a nonlinear one and motivates the choice for lumped nonlinear parameter model structures. It follows identification using nonlinear least squares in various liquids. The results obtained suggest that parameters of the proposed fractional order impedance model are susceptible to changes in density as being one important feature of non-Newtonian fluids. Further use of these findings across disciplines is given in the conclusion section of the paper.

In this paper, a fractional order representation and the related hardware realization of the Duffing system are presented. The considered system can be defined as the general case of the standard Duffing system, which is a typical example of a non autonomous chaotic system. The aim of this paper is to confirm that an opportunely excited fractional order Duffing system, with order less than two, can have a chaotic behavior. This statement is supported with the circuital implementation of the circuit.

The paper reports and compare some strategies to reduce high integer order transfer functions, exploiting both integer and non integer order calculus. In particular, the fractional order approach based on the implicit model, a genetic algorithm based procedure and the open loop balanced realization have been compared. Being fractional order system described in terms of pseudo-space, the number of the parameters of the reduced order models and their frequency response errors have been taken as performance indexes.

In this communication, the first example of fractional order nonlinear system showing jump resonance is described, that is a system in which the order of the derivative is non-integer and whose frequency response is a multi-valued function in a given range of frequencies. Furthermore, a strategy to design fractional order systems showing jump resonance is presented along with the procedure to design and implement an analog circuit based on the approximation of the fractional order derivative. An extensive numerical analysis allows to asses that the phenomenon is robust to difference in the derivative order, enlightening the fact that a system with order lower than two is able to provide a jump resonance behavior.

In this work a nonlinear dynamical model of a fuel cell stack is developed by means of artificial neural networks. The model presented is a black-box model, based on a set of easily measurable exogenous inputs like pressures and temperatures at the stack and is able to predict the output voltage of the fuel cell stack. The model obtained is being exploited as a component of complex control systems able to manage the energy flows between fuel cell stack, battery pack, auxiliary systems and electric engine in a zero-emission vehicle prototype

In this paper a CNNs based circuit for the generation of hyperchaotic signals is proposed. The circuit has been developed for applications in secure communication systems. An Saito oscillator has been designed by using a suitable configuration of a four-cells State-Controlled CNNs. A cryptography system based on the Saito oscillator has been implemented by using inverse system synchronization. The proposed circuit implementation and experimental results are given.

This paper proposes an experimental analysis on the convergence of evolutionary algorithms (EAs). The effect of introducing chaotic sequences instead of random ones during all the phases of the evolution process is investigated. The approach is based on the substitution of the random number generator (RNG) with chaotic sequences. Several numerical examples are reported in order to compare the performance of the EA using random and chaotic generators as regards to both the results and the convergence speed. The results obtained show that some chaotic sequences are always able to increase the value of some measured algorithm-performance indexes with respect to random sequences. Moreover, it is shown that EAs can be extremely sensitive to different RNGs. Some t-tests were performed to confirm the improvements introduced by the proposed strategy.

In this paper a new chaotic system based on two CNN-like cells is presented. Starting from the classical mathematical representation of Cellular Neural Networks, it is shown that the addition of a further parameter like the non-integer order m of each cell may cause the appearance of new complex behaviors. A suitable integer order approximation is therefore proposed and a chaotic circuit derived from it is realized.

The methodology proposed in the paper applies artificial intelligence (AI) techniques to the modeling and control of some climate variables within a greenhouse. The nonlinear physical phenomena governing the dynamics of temperature and humidity in such systems are, in fact, difficult to model and control using traditional techniques. The paper proposes a framework for the development of soft computing-based controllers in modern greenhouses.

A growing problem in town areas is noise pollution due to the increasing number of vehicles that daily cross cities. A classical approach to model this kind of system is based on numerical regression, but its performance is not satisfactory due to the nonlinearity of the considered model. A suitable approach can be therefore to determine a fuzzy model of the system. There has been a considerable number of studies on fuzzy identification, where fuzzy implications are used to express rules, in this paper the Tagaki-Sugeno approach has been adopted applying a genetic algorithm during the optimization phase. The obtained models are compared with traditional ones showing the suitability of the proposed method.

In this paper a new strategy for the design of fuzzy filters is proposed through the use of a genetic optimisation algorithm (GAs). The innovation of the proposed approach lies in the possibility to design fuzzy filters directly in the frequency domain. Comparisons between frequency responses of GAs fuzzy filters and traditional filters give satisfactory results, showing the validity of this technique.>