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A Genetic based Neuro-Fuzzy Controller System

by Mohamed, A. H
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 1
Year of Publication: 2014
Authors: Mohamed, A. H
10.5120/16306-5532

Mohamed, A. H . A Genetic based Neuro-Fuzzy Controller System. International Journal of Computer Applications. 94, 1 ( May 2014), 14-17. DOI=10.5120/16306-5532

@article{ 10.5120/16306-5532,
author = { Mohamed, A. H },
title = { A Genetic based Neuro-Fuzzy Controller System },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 1 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 14-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number1/16306-5532/ },
doi = { 10.5120/16306-5532 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:16:25.474528+05:30
%A Mohamed
%A A. H
%T A Genetic based Neuro-Fuzzy Controller System
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 1
%P 14-17
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recently, the mobile robots have great importance in the manufacturing processes. They are widely used for assembling processes, handling the dangerous components, moving the weighted things, etc. Designing the controller of the mobile robot is a very complex task. Many simple control systems used the neuro-fuzzy controller in the mobile robots. But, they faced with great complexity when moving in unstructured and dynamic environments. The proposed system introduces the uses of the genetic algorithm for optimizing the parameters of the neuro-fuzzy controller. So, the proposed system can improve the performance of the mobile robots. It has applied for a mobile robot used for moving the dangerous and critical materials in unstructured environment. Its results are compared with other traditional controller systems. The suggested system has proved its success for the real-time applications.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy controller Neuro-fuzzy Controller Optimization Modeling Genetic algorithm.

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