Title :
Neural control via Hopfield neural network
Author :
Lu Jin ; Xu Wenli ; Han Zengjin
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Abstract :
Adaptive control of a nonlinear time-variant system has been a dramatic problem attracting many scientists and engineers. As one part of the job of trying to solve the problem, a neural control method based on the Hopfield neural network (HNNMRAC) is presented in this paper. It achieves real-time control of practical plants. Theoretical problems are discussed and simulation results are given to shop the performance of this algorithm.<>
Keywords :
Hopfield neural nets; adaptive control; industrial computer control; model reference adaptive control systems; nonlinear control systems; time-varying systems; Hopfield neural network; adaptive control; algorithm performance; model reference adaptive controller; neural control; nonlinear time-variant system; practical plant control; real-time control; simulation results; Biological neural networks; Brain modeling; Delay effects; Equations; Hopfield neural networks; Neural networks; Neurofeedback; Neurons; Operational amplifiers; State feedback;
Conference_Titel :
TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on
Conference_Location :
Beijing, China
Print_ISBN :
0-7803-1233-3
DOI :
10.1109/TENCON.1993.320147
