Title :
Back-propagation neural networks for the inverse control of discrete-time nonlinear plant
Author :
Zeng-Ren, Yuan ; Xin-Gang, Guo
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
fDate :
29 June-1 July 1994
Abstract :
A backpropagation (BP) neural networks are applied to two kinds of inverse control methods for a discrete-time nonlinear plant. Two kinds of topological structure of BP neural networks are provided. Simulation results show that the tracking performance of a discrete-time nonlinear plant has been obtained satisfactorily in two kinds of input signal. In order to improve the tracking performance of the system, a combination of a variety of mapping functions is proposed. The learning and response of two networks have been developed in the Professional II plus neural networks. The program that connected in the neural networks is written in C language.
Keywords :
backpropagation; discrete time systems; neural nets; neurocontrollers; nonlinear systems; tracking; C language; Professional II plus; backpropagation neural networks; discrete-time nonlinear plant; inverse control; learning; mapping functions; topological structure; tracking performance; Artificial neural networks; Computational modeling; Computer science; Control systems; Equations; Erbium; Neural networks; Nonlinear control systems; Partial response channels; Uncertainty;
Conference_Titel :
American Control Conference, 1994
Print_ISBN :
0-7803-1783-1
DOI :
10.1109/ACC.1994.735107
