Research on Particle Swarm Optimsiation and its Application in Neural Network

Abstract:

This paper based on the PSO algorithm is a neural network model, and with other learning algorithm, and the results show that the performance comparisons are based on improved PSO algorithm two perceptron networks have higher classification accuracy and strong generalization ability. Particle Swarm Optimization (PSO) as an emerging evolutionary algorithm fast convergence speed, robustness, global search ability, and does not need the help of the characteristics of the problem itself (such as gradient). Combination of PSO and neural network PSO algorithm to optimize the connection weights of the neural network can be used to overcome the problem of BP neural network can not only play the generalization ability of the neural network, but also can improve the convergence rate of the neural network and learning capacity.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 5869-5872
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.5869
• Online since: May 2014
• Authors: Yue Li Li*, Shu Hui Chang
• Keywords: Application, Neural Network (NN), Particle Swarm Optimization (PSO), Veloctiy Muation

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