径向基函数(RBF)神经网络的一种极大熵学习算法

A Maximum-Entropy Learning Algorithm of Radial Basis Function(RBF) Neural Networks

RBF神经网络中心向量的确定是整个网络学习的关键 ,该文基于信息论中的极大熵原理构造了训练中心向量的极大熵聚类算法 ,由此给出了网络的极大熵学习算法 .文中最后分别用一个时间序列预测和系统辨识问题验证了该学习算法的有效性 ,同 RBF网络和多层感知机的误差回传算法相比 ,该算法不仅在学习精度和泛化推广能力上有一定程度的提高 ,而且学习时间有显著的降低 .

The key point in the design of RBF networks is to specify the number and the location of the centers. Several heuristic hybrid learning methods, which apply a clustering algorithm for locating the centers and subsequently a linear least squares method for the linear weights, have been previously suggested. A maximum entropy clustering method for training the center vectors is constructed via the maximum entropy principle in the information theory. Accordingly, a maximum entropy learning algorithm (MELA) of the RBF networks is given. Two experiments, including time series prediction and system identification, are given to test MELA. The results show, compared with the error back propagating algorithm of the multi layer perceptions (MLP) and RBF, MELA not only improves learning precision and generalization ability, but reduces learning time as well.