| 基于改进粒子群算法的隐马尔可夫模型训练 |
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| 引用本文： | 朱嘉瑜,高鹰.基于改进粒子群算法的隐马尔可夫模型训练[J].计算机工程与设计,2010,31(1). |
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| 作者姓名： | 朱嘉瑜 高鹰 |
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| 作者单位： | 1. 广州大学,数学与信息科学学院,广东,广州,510006
2. 广州大学,计算机与教育软件学院,广东,广州,510006 |
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| 摘 要： | 针对隐马尔可夫模型传统训练算法易收敛于局部极值的问题,提出一种带极值扰动的自适应调整惯性权重和加速系数的粒子群算法,将改进后的粒子群优化算法引入到隐马尔可夫模型的训练中,分别对隐马尔可夫模型的状态数与参数进优化.通过对手写数字识别的实验说明,提出的基于改进粒子群优化算法的隐马尔可夫模型训练算法与传统隐马尔可夫模型训练算法Baum-Welch算法相比,能有效地跳出局部极值,从而使训练后的隐马尔可夫模型具有较高的识别能力.
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| 关 键 词： | 粒子群优化算法 优化算法 隐马尔可夫模型 隐马尔可夫模型优化 手写数字识别 |
Adaptive particle swarm optimization for hidden Markov model training |
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| ZHU Jia-yu,GAO Ying.Adaptive particle swarm optimization for hidden Markov model training[J].Computer Engineering and Design,2010,31(1). |
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| Authors: | ZHU Jia-yu GAO Ying |
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| Affiliation: | ZHU Jia-yu1,GAO Ying2(1.College of Math , Information Science,Guangzhou University,Guangzhou 510006,China,2.College of Computer Science , Education Software,China) |
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| Abstract: | To solve the problem that easy to converge to local optimal solutions of hidden Markov model(HMM) training,a self-adaptive particle swarm optimization algorithm with disturbed extremum is presented and it is used in the training of HMM to optimize the state number and parameters of HMM.Comparing the proposed approach with Baum-Welch algorithm HMM training method,the hand-write digits recognition experimental results show that the proposed method is superior to the Baum-Welch training method and make the tra... |
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| Keywords: | particle swarm optimization optimization algorithm hidden Markov model hidden Markov model optimize handwrite digits recognition |
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