摘要
提出了ELM-RBF(extreme learning machine-radial basis function)神经网络的智能优化方法,采用差分进化算法和粒子群优化算法来确定ELM-RBF神经网络中隐层神经元的中心和宽度。仿真结果表明,在具有相同的网络结构前提下,基于智能优化策略的ELM-RBF神经网络学习算法具有更好的泛化能力和较好的鲁棒性。
A method of intelligent optimization strategy for extreme learning machine-radial basis function （ELM-RBF） neural networks was proposed,in which the centers and impact widths of hidden neural kernels were determined by the intelligent optimization algorithms of differential evolution and particle swarm optimization.Simulation results showed that the ELM-RBF neural networks learning algorithm based on the intelligent optimization strategy could generate much better generalization performance and robustness than other algorithms with the same network architecture.
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2010年第5期48-51,81,共5页
Journal of Shandong University(Natural Science)
基金
国家自然科学基金资助项目(60675044)
山东省重点基金资助项目(Z2007G02)
关键词
径向基函数神经网络
智能优化
差分进化算法
粒子群优化算法
radial basis function neural networks
intelligent optimization
differential evolution algorithm
particle swarm optimization algorithm
