| 基于PSO的RBF神经网络学习算法及其应用 |
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| 引用本文： | 张顶学,关治洪,刘新芝.基于PSO的RBF神经网络学习算法及其应用[J].计算机工程与应用,2006,42(20):13-15. |
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| 作者姓名： | 张顶学 关治洪 刘新芝 |
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| 作者单位： | 华中科技大学控制科学与工程系,武汉,430074 |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | 提出了一种基于粒子群优化(PSO)算法的径向基函数(RBF)神经网络学习方法,首先利用减聚类算法确定网络径向基层的单元数,再用PSO对基中心和宽度进行优化,并与最小二乘法相结合训练RBF神经网络。将此算法用于混沌时间序列的预测,实例仿真表明此方法是有效的。
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| 关 键 词： | 粒子群 径向基函数神经网络 减聚类算法 混沌时间序列 最小二乘法 |
| 文章编号： | 1002-8331-（2006）20-0013-03 |
| 收稿时间： | 2006-05 |
| 修稿时间： | 2006-05 |
RBF Neural Network Algorithm Based on PSO Algorithm and Its Application |
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| Zhang Dingxue,Guan Zhihong,Liu Xinzhi.RBF Neural Network Algorithm Based on PSO Algorithm and Its Application[J].Computer Engineering and Applications,2006,42(20):13-15. |
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| Authors: | Zhang Dingxue Guan Zhihong Liu Xinzhi |
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| Abstract: | This paper introduces a method of Radial Basis Function(RBF) neural network algorithm based on Particle Swarm Optimization(PSO) algorithm.The first to determine units' number in RBF layer using subtractive clustering method,the second to optimize central position and directional width based on PSO algorithm,and the last to train RBF neural network combine with least-square method.The new training algorithm is used to predict chaotic time series,and the simulation results are efficiency. |
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| Keywords: | particle swarm optimization RBF neural network subtractive clustering method chaotic time series least-square method |
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