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Data Pre-processing for a Neural Network Trained by an Improved Particle Swarm Optimization Algorithm

by Tuan Linh Dang, Thang Cao, Yukinobu Hoshino
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 1
Year of Publication: 2016
Authors: Tuan Linh Dang, Thang Cao, Yukinobu Hoshino
10.5120/ijca2016912022

Tuan Linh Dang, Thang Cao, Yukinobu Hoshino . Data Pre-processing for a Neural Network Trained by an Improved Particle Swarm Optimization Algorithm. International Journal of Computer Applications. 154, 1 ( Nov 2016), 1-8. DOI=10.5120/ijca2016912022

@article{ 10.5120/ijca2016912022,
author = { Tuan Linh Dang, Thang Cao, Yukinobu Hoshino },
title = { Data Pre-processing for a Neural Network Trained by an Improved Particle Swarm Optimization Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 1 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume154/number1/26452-2016912022/ },
doi = { 10.5120/ijca2016912022 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:59:01.460983+05:30
%A Tuan Linh Dang
%A Thang Cao
%A Yukinobu Hoshino
%T Data Pre-processing for a Neural Network Trained by an Improved Particle Swarm Optimization Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 1
%P 1-8
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes an improved version of particle swarm optimization (PSO) algorithm for the training of a neural network (NN). An architecture for the NN trained by PSO (standard PSO, improved PSO) is also introduced. This architecture has a data preprocessing mechanism which consists of a normalization module and a data-shuffling module. Experimental results showed that the NN trained by improved PSO (IPSO) achieved better performance than both the NN trained by standard PSO and the NN trained by back-propagation (BP) algorithm. The effectiveness concerning the recognition rate and the minimum learning error of the data preprocessing modules (normalization module, data-shuffling module) was also demonstrated through the experiments.

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Index Terms

Computer Science
Information Sciences

Keywords

Normalization Data shuffling Neural network Particle swarm optimization C language

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