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《Journal of Beijing University of Aeronautics and Astronautics》 2004-09
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Human-imitating control based on chaotic neural networks

Shi Xiaorong\ Zhang Minglian(School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China)  
A control method was proposed, which combines the human-imitating control theory and the optimization capability of chaotic neural networks (CNN). Firstly, a qualitative control law(linear or nonlinear control law)was formed according to human-imitating control. Then, the qualitative control law was quantified by CNN. Hopfield neural network is recognized as a useful tool for optimization problems. However, it is often trapped to a local minimum solution. Therefore, gradually reducing chaotic noise is added to the networks to form a powerful globe optimization algorithm. Applying the proposed control method to a double inverted pendulum, the results of numerical simulations and experiments both demonstrate the valid of this control method.
【Fund】: 国家自然科学基金资助项目 ( 60 0 740 2 1)
【CateGory Index】: TP273.5
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