基于小波神经网络的云模型

Cloud model based on wavelet neural networks

云模型是一种基于语言规则的不确定性推理系统.为了提高辨识精度通常需要增加规则数目,这样在多维输入的情况下容易形成"维数灾".为了解决此问题,利用小波神经网络代替传统云模型的后件隶属云,建立了一种基于小波神经网络的云模型(WNCM).详细分析了WNCM的系统结构,同时给出了参数和结构辨识算法.仿真结果以及与其它方法的对比分析表明,WNCM具有较强的非线性函数逼近能力,在不增加推理规则的前提下,可以实现对系统的精确辨识.

A cloud model is an uncertain inference system based on linguistic rules. The number of linguistic rules is usually increased to improve the accuracy of identification. The high-dimension of the input space will cause the curse of dimensionality. To solve this problem a wavelet network cloud model （WNCM） is proposed. A wavelet neural network is used to substitute the consequent part of the cloud model. The structure and learning algorithm of WNCM are designed. Simulation results and comparison with other methods indicate that WNCM can approximate arbitrary nonlinear functions. The accuracy of identification is realized without increasing linguistic rules.