基于在线减法聚类的RBF神经网络结构设计

以设计最小径向基函数（RBF）神经网络结构为着眼点,提出一种在线RBF网络结构设计算法.该算法将在线减法聚类能实时跟踪工况的特性与RBF网络参数学习过程相结合,使得网络既能在线适应实时对象的变化又能维持紧凑的结构,有效地解决了RBF神经网络结构自组织问题.该算法只调整欧氏距离距实时工况最近的核函数,大大提高了网络的学习速度.通过对典型非线性函数逼近和混沌时间序列预测的仿真,表明所提出的算法具有良好的动态特性响应能力和逼近能力.

Design RBF neural network architecture based on online subtractive clustering

This paper presents an online architecture design algorithm for radical basis function(RBF) neural network based on online subtractive clustering algorithm aiming at designing the minimal RBF neural network structure.The algorithm combines the characteristics that the online substractive clustering can track the real-time condition with the parameters learning process of the RBF neural network,which makes the RBF neural network adapt to the change of real-time condition dynamics while maintaining a compact network architecture.Therefore,this method can effectively solve the problem of self-organizing structure design of the RBF neural network.Only the kernel function whose Euclidean distance is nearest to the real-time conditions is adjusted,which greatly improves the learning speed of the network.The results of experiments on the typical function approximation and the chaotic time series prediction show that the algorithm owns favorable dynamic character response and approximating ability.