量子遗传神经网络语音水印算法  

Quantum Genetic Neural Network Based Speech Watermark Algorithm

作　　者：郝欢[1] 陈亮[1] 张翼鹏[2] 

机构地区：[1]解放军理工大学通信工程学院,江苏南京210007 [2]解放军南京炮兵学院训练部,江苏南京211123

出　　处：《信号处理》2013年第11期1476-1481,共6页Journal of Signal Processing

基　　金：国家自然科学基金资助项目(61072042)

摘　　要：传统的BP神经网络通常以梯度下降法作为训练搜索算法,极易陷入局部最优。本文将量子遗传算法引入到神经网络,提出了一种改进量子遗传算法优化BP神经网络系数的语音水印算法。首先利用改进量子遗传算法的良好全局搜索特性,优化BP神经网络的初始系数找出粗略解,然后采用梯度算法精细搜索出神经网络的最优权值和阈值系数,提高网络的收敛精度。理论分析和实验仿真表明,与传统的BP神经网络和遗传算法优化神经网络系数相比,本文提出的神经网络输出误差更小,有更大的水印容量。Traditional neural network always adopts gradient descent method as search method,which is easily fall into local optimum.Quantum genetic algorithm was introduced into neural network,and an improved quantum genetic neural network based speech watermark algorithm was proposed in this paper.Firstly,identify a rough solution by using the improved quantum genetic algorithm to optimize the initial coefficient of BP neural network.Then search out the optimal coefficient of BP neural network with the use of gradient descent method,thus raise the accuracy of network convergence.Theoretical analysis and experimental results show,compared with traditional BP network and GA based network,our method can achieve smaller error and bigger watermark capacity.

关 键 词：BP神经网络 梯度下降法 量子遗传算法 

分 类 号：TN912.3[电子电信—通信与信息系统]