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Comparative Study between the (BA) Algorithm and (PSO) Algorithm to Train (RBF) Network at Data Classification

by Ruba Talal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 92 - Number 5
Year of Publication: 2014
Authors: Ruba Talal
10.5120/16004-4998

Ruba Talal . Comparative Study between the (BA) Algorithm and (PSO) Algorithm to Train (RBF) Network at Data Classification. International Journal of Computer Applications. 92, 5 ( April 2014), 16-22. DOI=10.5120/16004-4998

@article{ 10.5120/16004-4998,
author = { Ruba Talal },
title = { Comparative Study between the (BA) Algorithm and (PSO) Algorithm to Train (RBF) Network at Data Classification },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 92 },
number = { 5 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 16-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume92/number5/16004-4998/ },
doi = { 10.5120/16004-4998 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:13:28.732232+05:30
%A Ruba Talal
%T Comparative Study between the (BA) Algorithm and (PSO) Algorithm to Train (RBF) Network at Data Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 92
%N 5
%P 16-22
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Swarm Intelligence Algorithms are (Meta-Heuristic) development Algorithms, which attracted much attention and appeared its ability in the last ten years within many applications such as data mining, scheduling, improve the performance of artificial neural networks (ANN) and classification. In this research was the work of a comparative study between Bat Algorithm (BA) and Particle Swarm Optimization Algorithm (PSO) to train Radial Basis function network (RBF) to classify types of benchmarking data. Results showed that Bat Algorithm (BA) is overcome on (PSO )Algorithm in terms of improving the weights of (RBF) network and accelerate the training time and good convergence of optimal solutions, which led to increase network efficiency and reduce falling mistakes and non-occurrence.

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Index Terms

Computer Science
Information Sciences

Keywords

RBF BA PSO ANN Meta-Heuristic.

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