基于改进GA的WRBF神经网络设计与应用

针对单独自动设计径向基函数(RBF)网络和小波网络过程中对样本要求过于严格,以及输出层线性求和运算可能造成样本类别交叠的问题,结合两种网络结构简单的优点,设计了一种新的四层前馈神经网络--小波径向基网络(Wavelet radial basis network,WRBF).该网络在结构上,第一隐层对输入样本进行小波映射,实现对输入空间的压缩;第二隐层对第一隐层输出进行第二次非线性映射;在网络的训练方法上,利用多阶染色体混合编码实现两隐层间的选择性连接,并对遗传算法(Genetic algorithm,GA)进行改进,利用改进的GA同时优化网络结构和参数.通过对多输入单输出系统和热能表系数模型进行实验,结果表明:改进的GA减小了早熟收敛的发生,所设计的网络具有较高的建模精度.

Design and Application of WRBF Neural Network Based on Improved GA

In order to overcome the demanding samples of designing RBF(radial basis function network) neural network and wavelet network automatically and the overlap phenomenon of linear summation in output layer,a new four-layer network named WRBF(wavelet radial basis function network) is designed based on the simple structures of RBF and wavelet network.In the network,the first hidden layer is used to compress input space and the second hidden layer is to process the compressed variables.In training methods,a high-rank chromosome for individual in improved GA is used to realize selecting connection between two hidden layers.The structure and parameters of WRBF network are optimized simultaneously.The experimental results from MISO system and coefficient model of thermal meter demonstrate that the improved GA reduces the premature phenomenon and that the WRBF network has higher precision for modeling.