基于动态自适应RBF网络的元音识别研究  

Vowel Classification Based on Dynamic Adaptive Radial Basis Function Network

作　　者：张东波[1] 

机构地区：[1]湘潭大学信息工程学院,湖南湘潭411105

出　　处：《计算机与现代化》2006年第7期15-17,共3页Computer and Modernization

摘　　要：鉴于BP网络训练时间过长,且易于陷入局部最优解,本文采用RBF网络来实现元音字母的语音识别。RBF网络的构造通过一种动态自适应聚类算法来完成,使得RBF网络具有在线学习能力。示例计算结果表明,这种RBF网络具有比BP网络和贝叶斯分类器更好的分类精度。BP network needs too much time to train and probably plunges into local optimization. A RBF network is designed to implement phonetic classification of the vowel character. The RBF network is designed by a dynamic adaptive clustering algorithm and the network has the ability of on-line learning. The test result indicates that the classification precision of the RBF network is better than that of BP network and Bayesian classifier.

关 键 词：径向基函数 神经网络 元音识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]