计算机工程与应用2012，Vol.48Issue(5)：37-40,4.DOI:10.3778/j.issn.1002-8331.2012.05.011
改进的粒子群算法对RBF神经网络的优化
Improved particle swarm optimization on RBF neural networks
摘要
Abstract
To improve the method to interpose structure and parameters of neural network, a novel Radial Basis Function(RBF) neural network method based on Improved Particle Swarm Optimization (IMPSO) is proposed. The convergence speed of this algorithm and the capacity of searching global optimum value are increased through adjusting inertia weight factor dynamically. The experiments show that the neural network based on IMPSO algorithm is superior to self-organizing center selected algorithm and standard PSO algorithm in the capacity of function approximation, and enhances the generalization and the optimized effect of the network. The capacity of solving nonlinear problems of this algorithm is enhanced effectively.关键词
粒子群算法/径向基神经网络/惯性权重因子Key words
Particle Swarm Optimization(PSO)/ Radial Basis Function Neural Network(RBFNN)/ inertia weight factor分类
信息技术与安全科学引用本文复制引用
夏轩,许伟明..改进的粒子群算法对RBF神经网络的优化[J].计算机工程与应用,2012,48(5):37-40,4.基金项目
上海市研究生创新基金项目(No.JWCXSL1022). （No.JWCXSL1022）
