A Chaos Artificial Fish Swarm Algorithm for Parameters Optimization and Application of Support Vector Machine

ZHU Wen-jing; BAI Jing

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A Chaos Artificial Fish Swarm Algorithm for Parameters Optimization and Application of Support Vector Machine

更新时间：2026-09-04

- A Chaos Artificial Fish Swarm Algorithm for Parameters Optimization and Application of Support Vector Machine
- Vol. 33, Issue 3, Pages: 90-94(2016)
- 作者机构：
  
  太原理工大学信息工程学院,山西,太原,030024
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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朱文静, 白静. 一种混沌人工鱼群算法对SVM参数的优化及应用[J]. 微电子学与计算机, 2016, 33(3): 90-94.

ZHU Wen-jing, BAI Jing. A Chaos Artificial Fish Swarm Algorithm for Parameters Optimization and Application of Support Vector Machine[J]. 2016, 33(3): 90-94.
朱文静, 白静. 一种混沌人工鱼群算法对SVM参数的优化及应用[J]. 微电子学与计算机, 2016, 33(3): 90-94. DOI：

ZHU Wen-jing, BAI Jing. A Chaos Artificial Fish Swarm Algorithm for Parameters Optimization and Application of Support Vector Machine[J]. 2016, 33(3): 90-94. DOI：

摘要

通过结合混沌模型实现对人工鱼群算法中各行为的改进

提出了一种混沌人工鱼群算法(CAFSA)优化SVM参数的方法

并采用测试函数进行测试和比较

再将寻优的参数运用到一个非特定人、孤立词的语音识别系统中.实验结果表明

在不同低信噪比和不同词汇量的条件下

基于混沌人工鱼群算法的SVM模型与基于基本人工鱼群算法的SVM模型相比

收敛速度明显加快

语音识别率也有不同程度的提高.

Abstract

This paper presents parameters of SVM were optimized by a chaos artificial fish swarm algorithm. By using some test functions

it is proved that combining with chaotic model can improve the behavior of artificial fish swarm algorithm. The optimized parameters are used into a speech recognition system of non-specific persons and isolated words. The experimental results show that convergence rates and speech recognition correct rates based on SVM using chaos artificial fish swarm algorithm are better than those by artificial fish swarm algorithm optimization parameters under different low SNRs and different words.

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references

袁小芳, 王耀南. 基于混沌优化算法的支持向量机参数选取方法[J]. 控制与决策, 2006, 21(1): 111-113. DOI: 111

高雷阜, 赵世杰, 高晶. 人工鱼群算法在SVM参数优化选择中的应用[J]. 计算机工程与应用, 2013, 49(23): 86-90. DOI: 86

邓乃扬, 田英杰. 数据挖掘中的新方法: 支持向量机[M]. 北京: 科学出版社, 2004.

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杨利红. 基于AFSA的SVM参数优化及其应用[D]. 太原: 太原理工大学, 2012.

李晓磊, 邵之江, 钱积新. 一种基于动物自治体的寻优模式: 鱼群算法[J]. 系统工程理论与实践, 2002, 22(11): 32-38. DOI: 32

李晓磊, 薛云灿, 路飞, 等. 基于人工鱼群算法的参数估计方法[J]. 山东大学学报, 2004, 34(3): 84-87. DOI: 84

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