DocumentCode
288694
Title
On-line neural network control applications
Author
Colina-Morles, Eliezer
Author_Institution
Fac. de Ingenieria, Los Andes Univ., Merida, Venezuela
Volume
4
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
2494
Abstract
The main objective of this work is to present an exploration of potential applications of online trained neural networks. In particular the work contains computer simulation results obtained from: implementing an internal model neural network-based control scheme, and a model reference neural network-based adaptive control scheme. The design of the online learning algorithm used to adapt the neural networks weights is based on the theory of continuous-time variable structure control systems. Both the selection of the neural network adaptation gain and the parameters of the low-pass filter involved in the scheme have been selected by trial and error. Only stable open-loop systems have been studied
Keywords
adaptive control; feedforward; filtering theory; learning (artificial intelligence); model reference adaptive control systems; neural nets; neurocontrollers; real-time systems; variable structure systems; adaptation gain; continuous-time variable structure control; internal model neural network-based control; low-pass filter; model reference neural network-based adaptive control; online learning; online neural network control; open-loop systems; Application software; Computer simulation; Control systems; Differential equations; Linear systems; Low pass filters; Neural networks; Open loop systems; Output feedback; Stability;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374612
Filename
374612
Link To Document