基宽灵敏度分析的径向基神经网络代理模型  

Surrogate model of radial basis function networks based on width factor sensitivity analysis

作　　者：张艳霞[1] 陈丹琪[1] 韩莹[1] 刘道华[2] 

机构地区：[1]防灾科技学院灾害信息工程系,北京101601 [2]信阳师范学院计算机与信息技术学院,河南信阳464000

出　　处：《智能系统学报》2014年第2期259-264,共6页CAAI Transactions on Intelligent Systems

基　　金：河北省教育厅高校科学技术研究计划资助项目(Z2013108);河北省科技计划资助项目(12270325);河南省教育厅科学技术研究重点资助项目(14A520001);河南省教师教育改革研究重点资助项目(2013-JSJYZD-025);廊坊市科学技术研究与发展计划资助项目(2013011021)

摘　　要：为提高径向基代理模型的近似精度,突破传统固定径向基基宽的方法,采用基宽灵敏度分析方法构建径向基代理模型,给出了基宽灵敏度分析的径向基代理模型具体参数的构建方法.采用Benchmark测试函数验证该代理模型的精度,并与采用固定径向基基宽的代理模型作测试对比,结果表明,采用该方法获得的代理模型在训练时间上多于其他方法,因模型参数获得时需要进行基宽灵敏度分析,但获得的模型精度比其他方法高,且该方法不需要更多的训练样本即能获得稳定的代理模型参数.In order to improve the approximation accuracy for the surrogate model of radial basis function networks and break through the traditional way that fixed the radial basis width factor, this paper adopts sensitivity analysis method on width factor to build the surrogate model of radial basis function networks, and gives the construction method of specific parameters for the surrogate model of radial basis function networks based on sensitivity analysis. The Benchmark test function is used to verify the accuracy of the surrogate model and the tested results are com- pared with the fixed radial basis width factor. The comparison results indicate that training time of the surrogate model obtained by this method is longer than that of other methods, because it needs sensitivity analysis when obtai- ning the width factor, while the obtained model accuracy is higher than other methods, besides, this method can obtainstable the surrogate model parameters without more training samples.

关 键 词：灵敏度分析 径向基函数神经网络 代理模型 基宽 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]