1.Deparment of Automation, University of Science and Technology of China, Hefei 230027 2.Industry Control Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016
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.
CHEN ZongHai,YUAN MingZhe,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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2006, Vol. 19 
