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

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

Tuan Linh Dang, Thang Cao and Yukinobu Hoshino. Data Pre-processing for a Neural Network Trained by an Improved Particle Swarm Optimization Algorithm. International Journal of Computer Applications 154(1):1-8, November 2016. BibTeX

@article{10.5120/ijca2016912022,
	author = {Tuan Linh Dang and Thang Cao and 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 = {November 2016},
	volume = {154},
	number = {1},
	month = {Nov},
	year = {2016},
	issn = {0975-8887},
	pages = {1-8},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume154/number1/26452-2016912022},
	doi = {10.5120/ijca2016912022},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, 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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Keywords

Normalization, Data shuffling, Neural network, Particle swarm optimization, C language