计算机工程Issue(10)：208-211,216,5.DOI:10.3969/j.issn.1000-3428.2013.10.044
基于RBF神经网络优化的混沌时间序列预测
Prediction of Chaotic Time Series Based on RBF Neural Network Optimization
摘要
Abstract
Based on neural network theory and phase-space reconstruction theory, a prediction algorithm for chaotic time series of optimized Radial Basis Function(RBF) neural based on Differential Evolution(DE) is proposed. In order to get the optimal neural network predictive model, the center, width, and connection weights of RBF neural networks are optimized by the global search ability of DE. The availability of the prediction algorithm is proved by the simulation of three typical nonlinear systems. Compared with the forecasting results of RBF neural network, results show that the improved algorithm has better generalization ability and higher forecasting accuracy.关键词
混沌时间序列/预测/径向基函数神经网络/差分进化算法/相空间重构/非线性系统Key words
chaotic time series/prediction/Radial Basis Function(RBF) neural network/Differential Evolution(DE) algorithm/phase-space reconstruction/nonlinear system分类
信息技术与安全科学引用本文复制引用
邬开俊,王铁君..基于RBF神经网络优化的混沌时间序列预测[J].计算机工程,2013,(10):208-211,216,5.基金项目
国家社科基金资助项目“突发事件应急物资调度模型及优化算法研究”(12CGL004)；甘肃省高等学校科研基金资助项目(2013A-052)；兰州交通大学青年科学研究基金资助项目(2011005) （12CGL004）
