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 :
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