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.
This paper is supported by the National Basic Research Program of China (973 Program), No.2004CB719405 and the National Natural Science Foundation of China, No. 50305008.
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Gao, L., Zhou, C., Gao, HB., Shi, YR. (2006). Combining Particle Swarm Optimization and Neural Network for Diagnosis of Unexplained Syncope. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_19
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DOI: https://doi.org/10.1007/11816102_19
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