基于混合径向基神经网络的建模及其逆模控制研究

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Abstract Traditional control methods are not satisfactory in more and more complex process control, and the generalization ability of neural networks in control is weak. In this paper, a novel structure, the combinations of the process fundamentals and RBFNN is presented to direct the neural network convergence and exert the excellent capability on nonlinear approach of neural networks. Simulation results show that the compute velocity of the backward model controller using the hybrid RBFNN, while the control precision index is ensured, is much higher than the backward model controllers using common RBFNN. The hybrid RBFNN backward model controller also has excellent control quality and shows good adaptation to disturbance, time delay, nonlinear and the drift of plant parameters.

Key words： Standard Radial Basis Function Neural Networks Hybrid Radial Basis Function Neural Networks Plant Mechanism Modeling Backward Model Control

Received: 29 August 2005

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	Articles by authors
	CHEN ZongHai
	YUAN MingZhe
	XIANG Wei
	ZHANG YanWu

Cite this article:

CHEN ZongHai,YUAN MingZhe,XIANG Wei等. Modeling Based on Hybrid Radial Basis Function Neural Networks and Its Backward Model Control[J]. , 2006, 19(6): 734-738.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2006/V19/I6/734

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