AN EFFICIENT ON-LIVE LEARNING METHOD FOR RADIAL BASIS FUNCTION NEURAL KETWORKS
径向基函数神经网络的一种有效的在线学习方法

This paper proposes an efficient on-line learning method for radial basis function (RBF) neural networks. The proposed learning method not only dynamically allocate the network resource in accordance with the increase of input Information, but also efficiently recycle the redundant resource of the network. During the learning process the parameters of the network can be sequentially adapted. The learning criterion, mechanism of increasing and decreasing resources and the parameter adjustment algorithm are elaborated. Meanwhile both the mapping approximation ability and predication performance of the network are analyzed in details.