粗糙RBF神经网络集成的模式识别方法

提出一种定义属性重要度的方法,并根据属性的重要度测量元素之间的距离,以确定训练集的聚类情况.由于聚类的不确定性,提出利用粗糙集方法确定精确的下、上近似集合,用其聚类中心作为RBF神经网络的径向基中心,设计两个基函数中心不同的RBF神经网络.最后在经验风险最小化原则下,确定两个网络的每个输出值的置信度,得到神经网络集成的最终输出.网络的训练采用递推最小二乘方法,通过两个模式识别仿真实例验证该方法的有效性和正确性.

Ensemble of Rough RBF Neural Networks for Pattern Recognition

A method of defining attribute importance is presented. In this method, the distance between samples can be measured to determine the training set clustering. The combination of two radial basis-function (RBF) neural networks for pattern recognition is proposed. The two RBF neural networks have different radial centers and they come from lower approximation and upper approximation of the clustering sets respectively. The designed rough approximation sets can solve the problem on uncertain clustering. Then, the two networks are combined under the experience risk minimum criterion. Thus, the different belief weights for outputs of neurons and the last neural networks output are determined. Finally, the simulation results of pattern recognition on UCI database show the proposed method is valid and effective.