信息与控制2017，Vol.46Issue(1)：60-64,5.DOI:10.13976/j.cnki.xk.2017.0060
基于盲动粒子群频率分解的极速学习机神经网络建模
ELM Neural Network Modeling Based on Frequency Decomposition with Blindfold Particle Swarm Optimization
摘要
Abstract
In order to improve the generalization of a neural network model,input frequency is divided into subbands.These sub-bands enhance information compactness,and those which cover the full frequency ensure ergodicity.Higher compactness and ergodicity help to improve the generalization of the neural network.Frequency decomposition is performed by particle swarm optimization with a blindfold feature.Because particle swarm optimization and the usual neural network algorithm require an iterative calculation,they take a long time for execution.An extreme learning machine neural network with a one-time iteration is time efficient.Simulation results show that the generalization and accuracy of the neural network model are higher and can satisfy the demands of general engineering applications.关键词
建模/极速学习机(ELM)神经网络/频率分解/盲动/粒子群优化Key words
modeling/ELM (extreme learning machine) neural network/frequency decomposition/blindfold/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
刘加存,梅其祥,杨东红..基于盲动粒子群频率分解的极速学习机神经网络建模[J].信息与控制,2017,46(1):60-64,5.基金项目
国家自然科学基金资助项目(61272534) （61272534）
