Synchronization of Chaotic Systems Via the Laguerre-Polynomials-Based Neural Network

摘要

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.