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。
To solve the problem that easy to converge to local optimal solutions of hidden Markov model （HMM） training, a selfadaptive 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 trained HMM has better recognition ability.
出处
《计算机工程与设计》
CSCD
北大核心
2010年第1期157-160,共4页
Computer Engineering and Design
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
particle swarm optimization
optimization algorithm
hidden Markov model
hidden Markov model optimize
hand- write digits recognition
