Title :
Approximate nonlinear system linearization with neural networks
Author :
Pei, Hai-Long ; Zhou, Qi-Jie
Author_Institution :
Dept. of Autom., South China Univ. of Technol., Guangzhou, China
Abstract :
Researches have shown that neural networks have the ability to approximate a function as well as its derivatives. In this paper a novel method of approximate nonlinear system linearization with neural networks is proposed. The network approximator is designed to integrate the involutive equation of a nonlinear system whether the integrability condition is satisfied or not. This result offers a promising opportunity to introduce neural network theory into nonlinear system control
Keywords :
approximation theory; control system synthesis; feedforward neural nets; function approximation; integration; linearisation techniques; multilayer perceptrons; nonlinear systems; partial differential equations; approximate nonlinear system linearization; integrability condition; involutive equation; network approximator; neural network theory; nonlinear system control; Artificial neural networks; Automation; Control systems; Linear approximation; Linear systems; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear systems; Partial differential equations;
Conference_Titel :
American Control Conference, 1997. Proceedings of the 1997
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3832-4
DOI :
10.1109/ACC.1997.611918
