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Improved PSO Algorithm for Training of Neural Network in Co-design Architecture

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Tuan Linh Dang, Yukinobu Hoshino

Tuan Linh Dang and Yukinobu Hoshino. Improved PSO Algorithm for Training of Neural Network in Co-design Architecture. International Journal of Computer Applications 182(44):1-7, March 2019. BibTeX

	author = {Tuan Linh Dang and Yukinobu Hoshino},
	title = {Improved PSO Algorithm for Training of Neural Network in Co-design Architecture},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2019},
	volume = {182},
	number = {44},
	month = {Mar},
	year = {2019},
	issn = {0975-8887},
	pages = {1-7},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2019918583},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This paper proposes a new version of the standard particle swarm optimization (SPSO) algorithm to train a neural network (NN). The improved PSO, called the wPSOd_CV algorithm, is the improved version of the PSOd_CV algorithm presented in a previous study. The wPSOd_CV algorithm is introduced to solve the issue of premature convergence of the SPSO algorithm. The proposed wPSOd_CV algorithm is used in a co-design architecture. Experimental results confirmed the effectiveness of the NN trained by the wPSOd_CV algorithm when compared with the NN trained by the SPSO algorithm and the PSOd_CV algorithm concerning the minimum learning error and the recognition rates.


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Neural network, Particle swarm optimization, FPGA, ARM, codesign architecture