模糊对向传播神经网络及其应用
Fuzzy Counter-Propagation Neural Network and its Application
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摘要: 通过把对向传播(CP)神经网络的竞争层神经元的输出函数定义为模糊隶属度函 数,提出了模糊对向传播(FCP)神经网络.该网络是CP网络的推广,它不仅能有效克服CP 存在的问题,而且具有全局函数逼近能力.在结构上,FCP网络同径向基函数(RBF)网络是等 价的.实际上,它是一种RBF网络,而且还是一种模糊基函数网络.FCP在时间序列预测中的 应用表明,FCP不仅在学习精度上,而且在泛化能力方面较之CP和RBF均有较大的改善.Abstract: A fuzzy counter-propagation (FCP) neural network, which is a generalized model of the counter-propagaton (CP) network, is proposed in this paper by defining output of the competitive unit of CP network as a fuzzy membership function. FCP not only is able to overcome the shortcomings of CP, but has the ability of universal function approximation as well. In view of network structure, FCP is equivalent to the radial basis function(RBF) network. In fact, FCP is an RBF network, also a fuzzy basis function network. In the end of this paper, the experiment to apply FCP to time series prediction shows that FCP outperforms CP and RBF in learning precision and generalizaton ability.
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