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基于信息强度的RBF神经网络结构设计研究

DOI: 10.3724/SP.J.1004.2012.01083, PP. 1083-1090

Keywords: 弹性RBF神经网络,结构设计,非线性系统,动态特征响应

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Abstract:

?在系统研究前馈神经网络的基础上,针对径向基函数(Radialbasisfunction,RBF)网络的结构设计问题,提出一种弹性RBF神经网络结构优化设计方法.利用隐含层神经元的输出信息(Output-information,OI)以及隐含层神经元与输出层神经元间的交互信息(Multi-information,MI)分析网络的连接强度,以此判断增加或删除RBF神经网络隐含层神经元,同时调整神经网络的拓扑结构,有效地解决了RBF神经网络结构设计问题;利用梯度下降的参数修正算法保证了最终RBF网络的精度,实现了神经网络的结构和参数自校正.通过对典型非线性函数的逼近与污水处理过程关键水质参数建模,结果证明了该弹性RBF具有良好的动态特征响应能力和逼近能力,尤其是在训练速度、泛化能力、最终网络结构等方面较之最小资源神经网络(Minimalresourceallocationnetworks,MRAN)、增长修剪RBF神经网络(GeneralizedgrowingandpruningRBF,GGAP-RBF)和自组织RBF神经网络(Self-organizingRBF,SORBF)有较大的提高.

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