| 基于RBF神经网络的高斯混合近似算法 |
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| 引用本文： | 樊国创,戴亚平,闫宁. 基于RBF神经网络的高斯混合近似算法[J]. 系统工程与电子技术, 2009, 31(10): 2489-2491,2526 |
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| 作者姓名： | 樊国创 戴亚平 闫宁 |
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| 作者单位： | 1. 北京理工大学自动控制系, 北京, 100081;2. 北京政法职业学院应用法律二系, 北京, 102600 |
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| 基金项目： | 中国学位与研究生教育学会"十一五"研究项目资助课题 |
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| 摘 要： | 在分析RBF神经网络基本结构的基础上,提出一种基于RBF神经网络求解非高斯概率密度近似为高斯概率密度和的方法.该方法通过选取高斯函数作为神经网络的径向基函数,提取训练好的网络参数,运用这些参数构建混合成分的函数模型.理论分析与仿真证明,与传统采用EM近似算法相比,该算法具有求解跟初值的选取无关、能避免发散、收敛快的特点.
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| 关 键 词： | RBF神经网络 高斯混合 EM算法 |
| 收稿时间： | 2008-04-11 |
| 修稿时间： | 2008-07-01 |
Gaussian mixture approximation algorithm based on radi us basis function neural network |
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| FAN Guo-chuang,DAI Ya-ping,YAN Ning. Gaussian mixture approximation algorithm based on radi us basis function neural network[J]. System Engineering and Electronics, 2009, 31(10): 2489-2491,2526 |
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| Authors: | FAN Guo-chuang DAI Ya-ping YAN Ning |
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| Affiliation: | 1. Dept. of Automation Control, Beijing Inst. of Technology, Beijing 100081, China;2. Second Dept. of Applied Law, Beijing Management Coll. of Politics and Law, Beijing 102600, China |
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| Abstract: | A algorithm based on radius basis function(RBF) neural network is presented,in which any nonlinear function can be approximated as a limited Gauss function mixture,on the basis of analysing the structure of RBF neural network.The Gauss function is selected as a radius basis function in the proposed algrithom,and the network parameters to have been trained are drawn and are used to build a mixture function.The results of theoretical analysis and simulation verify that the proposed algorithm is independent of initial values and is convergent rapidly compared with the traditional EM(expectation maximum) algorithm. |
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