Riccardo Caponetto

Chaotic sequences to improve the performance of evolutionary algorithms

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.

Fractional order systems : modeling and control applications

Fractional Order Systems Fractional Order PID Controller Chaotic Fractional Order Systems Field Programmable Gate Array, Microcontroller and Field Programmable Analog Array Implementation Switched Capacitor and Integrated Circuit Design Modeling of Ionic Polymeric Metal Composite

Bifurcation and Chaos in Noninteger Order Cellular Neural Networks

In this paper a new class of Cellular Neural Networks (CNNs) is introduced. The peculiarity of the new CNN model consists in replacing the traditional first order cell with a noninteger order one. The introduction of fractional order cells, with a suitable choice of the coupling parameters, leads to the onset of chaos in a two-cell system of a total order of less than three. A theoretical approach, based on the interaction between equilibrium points and limit cycles, is used to discover chaotic motions in fractional CNNs.

Soft computing for greenhouse climate control

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.

Power consumption reduction in a remote controlled street lighting system

In this paper a remote control equipment for monitoring and managing a street lighting system is presented. It is composed by a local control, realized by master boards located inside electrical panels and slave boards mounted on each lamppost, and by a remote control realized by a central unit for the remote communication with the local control system. The communication between master and slave boards is realized using power line modems, while the remote control central unit is connected to master boards via a GPRS-GSM communication. Master board allows to choice the electrical phase for the power line communication and to send the control commands to the slave boards on each lamppost. Slave boards allow to turn on/off the lamppost, to reduce power consumption using a device developed by ALBATROS Italia S.r.L., and to detect the lamp status, checking the current flow on the lamp itself.

A soft computing approach to fuzzy sky-hook control of semiactive suspension

This paper describes a soft computing approach to control a semiactive sky-hook suspension system. The fuzzy controller has been optimized by means of a genetic algorithm and implemented using a dedicated hardware device. After a preliminary study on a quarter car, a seven degree-of-freedom (7-DOF) model was implemented and used for controller simulation and optimization. The proposed control system takes the vehicle heave into account, improving the opposing requirements of comfort and drivability. The following conditions were optimized: drivability over random test roads, sine-wave holes, and sleeper-plates at different speeds. Fuzzy control makes it possible to change the characteristic parameters of the sky-hook suspension in an optimal way according to the different road conditions. The number of fuzzy controller inputs is kept as small as possible, avoiding the use of expensive sensors that increase the overall cost of the system. The proposed system is also easily applicable to existing commercial suspension systems. In addition to the comfort features, the fuzzy controller allows a reduction in load fluctuation, abnormal behavior and tire consumption.

SELF-ORGANIZATION IN NONRECURRENT COMPLEX SYSTEMS

In this paper, systems formed by networks of simple nonlinear cells are studied. Using lattice models, some of the fundamental features of complex systems such as self-organization and pattern formation are illustrated. In the first part of this work, a lattice of identical Chuas circuit is used to experimentally study its global spatiotemporal dynamics, according to the variation of some macroparameters, like the coupling coefficient or the neighboring dimension. The second part of the paper deals with the remarkable improvements regarding regularization and pattern formation, obtained in networks of nonlinear systems by introducing some spatial diversity, especially generated by deterministic, unpredictable dynamics. Simulation results show that synchronization and self-organization occur in networks with a few nonlocally connected cells, with irregular topology and small spatial diversity.

Fuzzy traffic smoothing: an approach for real-time communication over Ethernet networks

The paper presents an improvement on existing dynamic traffic smoothing techniques in two respects. Firstly, here the input parameters for the smoother are both the overall throughput and the number of collisions observed over an interval. Together, these two parameters represent a more complete indicator of the actual network workload. Secondly, here the smoothing action is dynamically gauged according to the actual workload by using a fuzzy controller. Experimental results in a real environment, comprising 11 workstations running the Linux OS and connected via a IOBASE-T Ethernet, are presented, together with a performance comparison with a dynamic smoother in the literature.

Cellular neural networks to explore complexity

Abstract In this paper the fundamentals of Cellular Neural Networks (CNNs) are introduced. Subsequently it is shown that, due to their locally distributed way of exchanging signals, such structures can be used as powerful devices to simulate and to reproduce, in an analog fashion and low cost, complex behaviors, i.e. dynamics commonly encountered in living systems, such as autonomous wave formation and propagation as well as morphogenetical pattern development. In fact it is proven that both of these behaviours can be simulated with CNNs with the same cell structure, and the thoroughly different dynamics can arise only suitably modulating the CNN cell parameters. Therefore a unifying approach to pattern formation and active wave propagation phenomena is presented. The derivation of the complex phenomena is analytically addressed and several simulation results are also reported.

Cellular neural networks in secure transmission applications

The work deals with a state controlled cellular neural network-based circuit for secure transmission applications. Basic principles of synchronisation between two (or more) chaotic systems are reported concerning the inverse system technique. Fundamentals of this kind of transmission are briefly introduced together with some experimental results. Finally, programmability of CNN circuits is exploited in order to increase the transmission security allowing the possibility to set-up several encryption/decryption key codes.