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10.5120/ijca2019918583 |
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
@article{10.5120/ijca2019918583, 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 = {http://www.ijcaonline.org/archives/volume182/number44/30442-2019918583}, doi = {10.5120/ijca2019918583}, publisher = {Foundation of Computer Science (FCS), NY, USA}, address = {New York, USA} }
Abstract
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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Keywords
Neural network, Particle swarm optimization, FPGA, ARM, codesign architecture