DocumentCode
2505110
Title
A fast recursive neuronal learning algorithm applied to intelligent control
Author
Garcia-Padilla, F. ; Morant-Anglada, F. ; Martinez-Iranzo, M.
Author_Institution
Centro de Sistemas Digitales e Inf. Ind., Univ. de Oriente-Nucleo de Anzoategui, La Cruz
fYear
1994
fDate
12-14 Apr 1994
Firstpage
653
Abstract
This paper presents as a contribution the formulation of the recursive least squares neuronal (RLSN) learning algorithm for teaching a neural network of the multilayer perceptron type. With this algorithm, the time of convergence achieved in the neural network is less than the time of convergence achieved when teaching the network with gradient methods. The procedure allows the application of neural network techniques to nonlinear and time dependant systems which are difficult to model and control
Keywords
intelligent control; learning (artificial intelligence); multilayer perceptrons; neural nets; recursive estimation; convergence; intelligent control; multilayer perceptron; neural network; nonlinear systems; recursive least squares neuronal learning; time dependant systems; Computer networks; Convergence; Education; Intelligent control; Least squares methods; Multi-layer neural network; Multilayer perceptrons; Neural networks; Neurons; Nonlinear control systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrotechnical Conference, 1994. Proceedings., 7th Mediterranean
Conference_Location
Antalya
Print_ISBN
0-7803-1772-6
Type
conf
DOI
10.1109/MELCON.1994.381006
Filename
381006
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