一种结构自适应的径向基函数神经网络

提出了一种新的结构自适应的径向基函数(RBF)神经网络模型。在该模型中,自组织映射(SOM)神经网络作为聚类网络,采用无监督学习算法对输入样本进行自组织分类,并将分类中心及其对应的权值向量传递给RBF神经网络,分别作为径向基函数的中心和相应的权值向量;RBF神经网络作为基础网络,采用高斯函数实现输入层到隐层的非线性映射,输出层则采用有监督学习算法训练网络的权值,从而实现输入层到输出层的非线性映射。通过对字母数据集进行仿真,表明该网络具有较好的性能。

Radial basis function neural network with self-adaptive structure

In this paper,the model of adaptive radial base function neural network is presented.In this model,SOM neural network,as a cluster network,performed unsupervised learning and weight vectors belonging to its output nodes are transmitted to the hidden nodes in RBF network as the centers of RBF activation function,as a result one to one correspondence relationship is realized between the output nodes in SOM and the hidden nodes in RBF network.RBF network,as a basic network,performed the nonlinear mapping from input nodes to hidden nodes using Gauss function and linear mapping from hidden nodes to output nodes using supervised learning algorithm.The results of simulation on the recognition of the set of English character are shown to prove the proposed networks have good performance.