Local response neural networks and fuzzy logic for control

Author

Geva, Shlomo ; Sitte, Joaquin

Author_Institution

Fac. of Inf. Technol., Queensland Univ. of Technol., Brisbane, Australia

fYear

1993

fDate

27-29 Sep 1993

Firstpage

51

Lastpage

57

Abstract

It is shown how to build and train multilayer perceptrons for the approximation of control functions. The special class of perceptrons called local response networks have the advantage that they train much faster than the general multilayer perceptrons (MLPs), and that the accuracy of the approximation can be increased by adding more neurons without the need of global retraining. They also have the advantage that the knowledge of a trained network is easily translated into rules, similar to fuzzy logic

Keywords

function approximation; fuzzy control; fuzzy logic; learning (artificial intelligence); multilayer perceptrons; neurocontrollers; control function approximation; fuzzy logic; global retraining; local response networks; multilayer perceptrons; neural networks; rules; trained network; training; Error correction; Function approximation; Fuzzy control; Fuzzy logic; Interpolation; Knowledge engineering; Lapping; Multi-layer neural network; Neural networks; State-space methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Emerging Technologies and Factory Automation, 1993. Design and Operations of Intelligent Factories. Workshop Proceedings., IEEE 2nd International Workshop on

Conference_Location

Palm Cove-Cairns, Qld.

Print_ISBN

0-7803-0985-5

Type

conf

DOI

10.1109/ETFA.1993.396430

Filename

396430