A Fuzzy Classifier Based on the Constructive Covering Approach in Neural Networks
一种基于神经网络覆盖构造法的模糊分类器

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.