特征加权模糊感知器算法

Weighted Feature Fuzzy Perceptron Algorithm

本文提出了一种改进的模糊感知器算法,基本思想源于在学习中观察在旋转、位移和尺寸变换下,由矩不变量构成的特征矢量其各分量的变化是不同的。用方差对特征矢量进行加权,使变化小的特征分量在分类中起主要作用。模拟结果表明:新算法较原算法在分类中更为有效。

An improved fuzzy perceptron algorithm is suggested for the two-class classification. The basic idea originates from an observation that the variances of different moment invariants (features) under rotation, shift and size transform are not the same. Then the sample feature vectors are weighted, so that those features with small variance will play the dominant part. The algorithm is also extended to multiclass case and the experimental results are given. The computer simulation shows that the new method is more effective for the classification than the old one.