Recently, Particle Swarm Optimization(PSO) has been widely applied for training neural network. To improve the performance of PSO for high-dimensional solution space which always occurs in training NN, this paper introduces a new paradigm of particle swarm optimization named stochastic PSO (S-PSO). The feature of the S-PSO is its high ability for exploration. Consequently, when swarm size is relatively small, S-PSO performs much better than traditional PSO in training of NN. Hence if S-PSO is used to realize training of NN, computational cost of training can be reduced significantly. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Li, Y., & Chen, X. (2006). A new stochastic PSO technique for neural network training. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3971 LNCS, pp. 564–569). Springer Verlag. https://doi.org/10.1007/11759966_84
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