一种基于神经网络覆盖构造法的模糊分类器

首先介绍了一种M-P模型几何表示,以及利用这种几何表示可将神经网络的训练问题转化为点集覆盖问题,并在此基础上分析了神经网络训练的一种几何方法.针对该方法可构造十分复杂的分类边界,但其时间复杂度很高.提出一种将神经网络覆盖算法与模糊集合思想相结合的方法,该分类器可改善训练速度、减少覆盖的球领域数目,即减少神经网络的隐结点数目.同时模糊化方法可方便地为大规模模式识别问题提供多选结果.用700类手写汉字的识别构造一个大规模模式识别问题测试提出的方法,实验结果表明,该方法对于大规模模式识别问题很有潜力.

A Fuzzy Classifier Based on the Constructive Covering Approach in Neural Networks

A geometrical representation of M-P model is firstly introduced, by which the training problem of neural networks may be transformed into the covering problem of a point set. According to this, the geometrical algorithm of neural network training is analyzed. The algorithm may be used for constructing very complicated classifying boundary, but it has higher time complexity. So a fuzzy classifier based on the combination of the covering approach and fuzzy set theory is proposed. The classifier can improve the speed of training and decrease the number of covering sphere-neighborhoods, i.e., decrease the number of hidden nodes of neural networks. The fuzzy set based approach may also provide multi-choices for pattern recognition problems of large scale. Recognition of 700 handwritten Chinese characters is used to test the performance of the approach and the results are promising.