Catálogo de publicaciones - libros

In recent years, chaos synchronization has attracted many researchers’ interests. For a class of chaotic synchronization systems with unknown uncertainties caused by both model variations and external disturbances, an orthogonal function neural network is utilized to realize the synchronization of chaotic systems. The basis functions of orthogonal function neural network are Laguerre polynomials. First of all, the orthogonal function neural network is trained to learn the uncertain information. Then, the parameters of Laguerre orthogonal neural network are adjusted to accomplish the synchronization of two chaotic systems with the perturbation by Lyapunov steady theorem. At last, the result of numerical example is shown to illustrate the validity of the proposed method.

This work presents chaos synchronization between two different chaotic systems via active control and adaptive control. Synchronization between unified chaotic system and Genesio system are investigated, different controllers are designed to synchronize the drive and response systems, Numerical simulations show the effectiveness of the proposed schemes.

In this paper, we study the effect of parameter mismatches on the fuzzy neural networks with impulses. Since it is impossible to make two non-identical neural networks complete synchronized, we study the synchronization of two neural networks in terms of quasi-synchronization. Using Lyapunov method and linear matrix inequality method, we obtain a sufficient condition for a global synchronization error bound of the two neural networks.

In this paper, synchronization is investigated for an array of nonlinearly coupled identical connected neural networks with delay. By employing the Lyapunov functional method and the Kronecker product technique, several sufficient conditions are derived. It is shown that global exponential synchronization of the coupled neural networks is guaranteed by a suitable design of the coupling matrix, the inner linking matrix and some free matrices representing the relationships between the system matrices. The conditions obtained in this paper are in the form of linear matrix inequalities, which can be easily computed and checked in practice. A typical example with chaotic nodes is finally given to illustrate the effectiveness of the proposed synchronization scheme.

The application of sinusoidal periodic search signals into the general extremum seeking algorithm(ESA) results in the “chatter” problem of the output and the switching of the control law and incapability of escaping from the local minima. An improved chaotic annealing recurrent neural network (CARNN) is proposed for ESA to solve those problems in the general ESA and improve the global searching capability. The paper converts ESA into seeking the global extreme point where the slope of Cost Function is zero, and applies a CARNN to finding the global point and stabilizing the plant at that point. ESA combined with CARNN doesn’t make use of search signals such as sinusoidal periodic signals, which solves those problems in previous ESA and improves the dynamic performance of the controlled system greatly. During the process of optimization, chaotic annealing is realized by decaying the amplitude of the chaos noise and the probability of accepting continuously. The process of optimization was divided into two phases: the coarse search based on chaos and the elaborate search based on ARNN. At last, CARNN will stabilize the system to the global extreme point. At the same time, it can be simplified by the proposed method to analyze the stability of ESA. The simulation results of a simplified UAV tight formation flight model and a typical Schaffer function validate the advantages mentioned above.

Delay constrained multicast routing (DCMR) aims to construct a minimum-cost tree with end-to-end delay constraints. This routing problem is becoming more and more important to multimedia applications which are delay-sensitive and require real time communications. We solve the DCMR problem by the transiently chaotic neural network (TCNN) of Chen and Aihara. Simulation results show that the TCNN is more capable of reaching global optima compared with the Hopfield neural network (HNN).

A secure media distribution scheme is proposed in this paper, which distributes different copy of media content to different customer in a secure manner. At the sender side, media content is encrypted with a chaotic neural network based cipher under the control of a secret key. At the receiver side, the encrypted media content is decrypted with the same cipher under the control of both a secret key and the customer information. Thus, the decrypted media copy containing customer information is slightly different from the original one. The difference can be detected and used to trace media content’s illegal distribution. The scheme’s performances, including security, imperceptibility and robustness, are analyzed and tested. It is shown that the scheme is suitable for secure media distribution.

In the proposed new scheme of adaptive radar target signal processing, the chaotic neural network not only detects the target signal by reconstructing the chaotic clutter, but also repairs the frequency spectrum according to its associative memory characteristic. The clutter is filtered by the Burg algorithm based on the adaptive MTI filter. The information of distance and velocity is also obtained by Burg spectral estimation. The validity of the scheme is analyzed theoretically, and the simulation results show that it has good performance in clutter and noise background. The adaptive method adopted in this paper facilitates the radar design in complex environment.

This paper studies the hyperchaotic dynamics in a four dimensional Hopfield neural network. A topological horseshoe on a three dimensional block is found in a carefully chosen Poincaré section hyperplane of the ordinary differential equations. Numerical studies show that there exist two-directional expansions in this horseshoe map. In this way, a computer-assisted verification of hyperchaoticity of this neural network is presented by virtue of topological horseshoe theory.

The parametrically coupled map lattice (PCML) exhibits many interesting dynamical behaviors that are reminiscent of the adaptation and the learning of the neural network. In order for the PCML to be a model of the neural network, however, it is necessary to identify the biological counterpart of one-dimensional maps that constitute the PCML. One of the possible candidates is a netlet, a small population of randomly interconnected neurons, that was suggested to be a functional unit constituting the neural network. We studied the possibility of representing a netlet by a chaotic one-dimensional map and the result is the chaotic netlet map that we introduce in this paper.