基于N进制编码的克隆重组方法应用于快速优化ADPCM的多参数

TN911.7; 选择ADPCM的步长修正因子M涉及复杂目标函数的多变量优化,是一个海量计算问题.克隆-思维进化算法(CMEA)继承了进化算法(MEA)中趋同操作的择强汰弱思想,同时引入克隆算子将相对较弱个体的强势因素保留到下一代.提出一种新的基于N进制编码的克隆重组方法用于优化具有复杂目标函数的ADPCM的8个步长修正因子,实验结果表明从第5次迭代后,CMEA的信噪比(SNR)一直高于MEA算法;在前5次迭代中,CMEA平均每次迭代改善1.03dB,高出MEA算法0.4dB.此外MEA的计算量约为CMEA的1.67倍,CMEA比MEA算法抗早熟....

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Published inTongxin Xuebao Vol. 27; no. 3; pp. 28 - 31
Main Authors 张刚, 谢克明, 郭红波, 赵哲峰
Format Journal Article
LanguageChinese
Published 太原理工大学,信息工程学院,山西,太原,030024 2006
Editorial Department of Journal on Communications
Subjects
N进制编码
多变量优化
克隆-思维进化
复杂目标函数
Online AccessGet full text
ISSN1000-436X
DOI10.3321/j.issn:1000-436X.2006.03.004

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Abstract TN911.7; 选择ADPCM的步长修正因子M涉及复杂目标函数的多变量优化,是一个海量计算问题.克隆-思维进化算法(CMEA)继承了进化算法(MEA)中趋同操作的择强汰弱思想,同时引入克隆算子将相对较弱个体的强势因素保留到下一代.提出一种新的基于N进制编码的克隆重组方法用于优化具有复杂目标函数的ADPCM的8个步长修正因子,实验结果表明从第5次迭代后,CMEA的信噪比(SNR)一直高于MEA算法;在前5次迭代中,CMEA平均每次迭代改善1.03dB,高出MEA算法0.4dB.此外MEA的计算量约为CMEA的1.67倍,CMEA比MEA算法抗早熟.
AbstractList TN911.7; 选择ADPCM的步长修正因子M涉及复杂目标函数的多变量优化,是一个海量计算问题.克隆-思维进化算法(CMEA)继承了进化算法(MEA)中趋同操作的择强汰弱思想,同时引入克隆算子将相对较弱个体的强势因素保留到下一代.提出一种新的基于N进制编码的克隆重组方法用于优化具有复杂目标函数的ADPCM的8个步长修正因子,实验结果表明从第5次迭代后,CMEA的信噪比(SNR)一直高于MEA算法;在前5次迭代中,CMEA平均每次迭代改善1.03dB,高出MEA算法0.4dB.此外MEA的计算量约为CMEA的1.67倍,CMEA比MEA算法抗早熟.
Author 张刚
郭红波
谢克明
赵哲峰
AuthorAffiliation 太原理工大学,信息工程学院,山西,太原,030024
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ZHANG Gang
GUO Hong-bo
ZHAO Zhe-feng
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多变量优化
克隆-思维进化
复杂目标函数
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Snippet TN911.7; 选择ADPCM的步长修正因子M涉及复杂目标函数的多变量优化,是一个海量计算问题.克隆-思维进化算法(CMEA)继承了进化算法(MEA)中趋同操作的择强汰弱思想,同时引入克...
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Title 基于N进制编码的克隆重组方法应用于快速优化ADPCM的多参数
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