基于改进学习的BP网络声音特征信号识别  

Sound Signal Recognition Based on BP Network With Improved Learning Algorithm

作　　者：崔峰[1] 邱忠阳[1] 陈春雨[1] 

机构地区：[1]大庆师范学院物理与电气信息工程学院,黑龙江大庆163712

出　　处：《工业控制计算机》2012年第9期104-105,139,共3页Industrial Control Computer

基　　金：大庆师范学院科学研究基金(10ZR09)

摘　　要：建立了一个三层前向神经网络对四种声音信号进行识别分类,网络采用改进学习的BP算法训练,即在最速下降法训练的基础上,引入了MOBP动量因子和学习率调整。仿真验证结果表明,所设计的BP网络识别分类误差小,识别正确率高。A three layer feedforward neural network is developed for sound recognition which is made up by four kinds of sound in this paper.Based on the steepest descent method,the BP network is trained by an improved learning algorithm in which momentum factor and learning rate adjustment is adopted.The simulation results indicates that the error in classification small also with high recognition correct in terms of a percentage.

关 键 词：声音识别 BP网络 MOBP 变学习率 

分 类 号：TN912.34[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]