一种改进的神经网络板形模式识别方法

本文提出了一种改进的神经网络板形模式识别方法,该方法基于支持向量机(SVM)与径向基(RBF)网络的结构等价性,利用SVM的回归确定RBF网络较优的初始参数,解决了传统神经网络模式识别方法存在的网络学习时间长,易陷入局部极小值等问题。此外,由于板形标准模式具有两两互反性,将输入样本与基本模式的模糊距离差作为网络输入,使输入节点减少一半,近一步实现了网络结构的固定化和简单化。实验表明,它提高了板形识别精度和速度,可推广到其他标准模式具有两两互反性的模式识别中。

An Improved Approach of Neural Network Flatness Pattern Recognition

The Improved approach has been proposed based on the structural equivalence of radial basis function (RBF) network and Support Vector Machines (SVM). The optimal initial parameters of RBF network were gained through SVM regression, which has solved problems of the traditional method known as neural network with slow convergence and local minimum etc. Moreover, accord- ing to the reciprocal characteristic of every two typical patterns, the deduction of fuzzy distance measure was applied, which has got the numbers of the inputs declined by a half and developed the realization of the changeless and simple structure of the neural net- work. The improved RBF network approach to flatness pattern recognition based on SVM learning has been proved with high preci- sion and speed. It could also be put into other fields in which Reciprocal polynomials for every two typical patterns are existed.