Chaotic Control Resrarch Based On Neural Network

The control of chaotic systems has capacious application foreground, a great number of method for the control of chaotic systems has been brought out. However, control theory of chaotic systems is still not perfect so far because of the complexity and particularity of the dynamic behavior in chaotic systems. Especially when the mathematic model of the system cannot be actually constructed and little preconditions be attained, classic control methods not act that well. Since 1980's, the research of neural networks has made great progresses, which has already proved that the neural networks ean approach any continuous nonlinear function. Characteristics of neural networks make it has considerable potential when be used to control a system which is highly nonlinear or uncertain.In this paper, research concentrates on the neural network control of chaotic systems in electronics and circuits systems, including two fields: chaotic systems identification and chaotic control.Firstly, based on the analysis of advantages and disadvantages of basic back-propagation neural network and ameliorating arithmetic in the process of systems' identification, a new ameliorating arithmetic which is the combination of additional momentum and adaptive momentum method has been presented, and then identifying Lorenz chaotic system is carefully studied by using of the new method. Results of the numerical simulation demonstrate that the newly presented scheme is of great effectiveness.Secondly, several kind of neural network controlling structures which apply to chaotic systems are given out, adopted reference model and self-adaptive controlling structure are consulted. This paper putmuch energy into the simulation of B-spline neural network in Lorenz chaotic system. Results of computer simulation clearly show that this method is much more effective as compared whit back-propagation neural networks.At last, in order to control unknown multivariable chaotic systems effectively, dynamical neural network is employed to control the unknown multivariable Lorenz chaotic system. Computer simulation elementary demonstrated the application feasibility of dynamical neural network in chaotic control.