Combining particle swarm optimization and neural network for diagnosis of unexplained syncope

Authors:
Liang Gao;Chi Zhou;Hai-Bing Gao;Yong-Ren Shi
Affiliations:
Department of Industrial & Manufacturing System Engineering, Huazhong Univ.of Sci. & Tech., Wuhan, China;Department of Industrial & Manufacturing System Engineering, Huazhong Univ.of Sci. & Tech., Wuhan, China;Department of Industrial & Manufacturing System Engineering, Huazhong Univ.of Sci. & Tech., Wuhan, China;Department of Industrial & Manufacturing System Engineering, Huazhong Univ.of Sci. & Tech., Wuhan, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Year:
2006

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Abstract

Given the relative limitations of BP and GA based leaning algorithms, Particle Swarm Optimization (PSO) is proposed to train Artificial Neural Networks (ANN) for the diagnosis of unexplained syncope. Compared with BP and GA based training techniques, PSO based learning method improves the diagnosis accuracy and speeds up the convergence process. Experimental results show that PSO is a robust training algorithm and should be extended to other real-world pattern classification applications.