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

一种混沌人工鱼群算法对SVM参数的优化及应用

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

  • 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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