计算机技术与发展Issue(8)：181-184,4.DOI:10.3969/j.issn.1673-629X.2013.08.046
一种混沌混合粒子群优化RBF神经网络算法
An Algorithm of Chaotic Hybrid Particle Swarm Optimization Based on RBF Neural Network
摘要
Abstract
In order to detect the weak target signal accurately in the chaos background, and improve forecast result, a novel algorithm based on RBF Neural Network ( RBFNN) with Chaotic Hybrid Particle Swarm Optimization ( CHPSO) is presented. In this algorithm, the RBF neural network is optimized by chaotic particle swarm optimization with adaptive population mutation and individual annealing operation. In order to improve the global convergence ability of PSO,the colony adaptive mutation and individual annealing operation are used to adjust and optimize PSO. Then the parameters and structures of RBFNN are optimized. This novel algorithm is applied to predict chaotic time sequence and detect weak target signal in the chaos background. Simulation results show that the algorithm has preferable nonlinear prediction ability and can detect weak target signal effectively.关键词
混沌/自适应变异/粒子群/模拟退火/RBF神经网络/目标检测Key words
chaos/adaptive mutation/particle swarm/simulated annealing/RBF neural network/target detection分类
信息技术与安全科学引用本文复制引用
刘洁,李目,周少武..一种混沌混合粒子群优化RBF神经网络算法[J].计算机技术与发展,2013,(8):181-184,4.基金项目
国家自然科学基金资助项目(60673119) （60673119）
国家863/CIMS项目(2006AA04Z152) （2006AA04Z152）
湖南省科技计划项目(2006GK3071) （2006GK3071）
湖南省教育科研项目(10C0672) （10C0672）
