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Chaotic System Control Via Self-constructing Neural Network

Author: WangXinBo
Tutor: ZhangGuoShan
School: Tianjin University
Course: Control Theory and Control Engineering
Keywords: Chaos Control Nonlinear systems Chaos synchronization Neural Networks
CLC: TP183
Type: Master's thesis
Year: 2010
Downloads: 91
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Abstract


Chaos theory is an integral part of nonlinear science , as a natural phenomenon , chaos sometimes give practical things disturbance even damage , such as the power grid in the chaotic greatly impact the effectiveness of the system , a more serious cause electricity the collapse of the system , for these chaotic phenomena , we should be to control and eliminate the OGY control theory proposed to raise awareness of the chaos can be controlled . Artificial neural networks has been proven approximation of the characteristics of the nonlinear system any fitting characteristics and generalization ability of neural network control for certain nonlinear systems can be achieved ; combine artificial neural networks and chaotic systems , scholars have proposed some chaotic systems based on artificial neural networks control method , the use of a self- constructed neural network control of chaotic systems and synchronization . Neural network control in-depth study , the use of neural networks achieve good control and synchronization of the types of chaotic systems , and a reversing nonlinear system control . The main work is as follows : (1) an improved self- constructed wavelet neural network controller , using the measuring method (dmm) and references adjustable threshold screening of hidden layer neurons , so that the choice of the network structure tends to be more rationalization , and adjustable threshold adaptive adjustment makes the whole algorithm more intelligent . We use this controller to the car reversing warehousing , simulation results show that the controller has superior performance . ( 2 ) the use of the OGY method , neural network , and improved self- constructed neural network control of chaotic systems . Controller design , stability analysis , and compared the effect of their control . We give improved self - constructed RBF network containing uncertain chaotic systems control , simulation results show that the control method is feasible . ( 3) gives the chaotic synchronization based on the feedback method , and neural network - based Chaos synchronization of the two methods were compared . The neural network section gives a complete control program , and system stability analysis and simulation .

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