Nootyasakool, Supakit

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Nootyasakool, Supakit
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Nootyaskool, Supakit
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supakit.no@kmitl.ac.th

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    Hybrid Genetic Algorithm with Baum-Welch Algorithm by using diversity population technique
    (2006-12-01)
    Nootyaskool, Supakit
    ;
    Kruatrachue, Boontee
    Baum-Welch Algorithm (BWA) have used in recognition systems, many researchers have improved BWA performances by using Hybrid Genetic Algorithm (HGA). This paper presents a new HGA technique by using diversity population structure. We surveyed HGA techniques and divided into four types. There were separate processes, population types, fitness determiners, and diversity population structure. A technique of diversity population structure protected applying BWA to similar population. Different population structures make available GA to find optimum point quickly. This paper compared all of HGA techniques, which there trained on Hidden Markov Models (HMM), in an application Thai off-line handwritten recognition, we used database from NECTEC. An experiment of HGA, HMM probability of diversity population techniques get better than techniques of population types 52.65% improvement and there better than techniques of separately process 37.91% improvement. Moreover, HGA experimented five times repeatedly, standard derivation value of diversity population techniques showed closely results. © 2006 IEEE.
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    HMM Parameters Optimization using Combine Genetic Algorithm and Iterative Training
    (2003-12-01)
    Kruatrachue, Boontee
    ;
    Siriboon, Kritawan
    ;
    Nootyaskool, Supakit
    HMM have been used extensively for recognizing observation sequence especially in speech recognition. Iterative training procedure such as Baum-Weltch, or gradient techniques are normally used to find locally optimize HMM parameters. This paper presents genetic algorithm (GA) to perform global search for Hidden Markov Model (HMM) parameters that maximize probability of observation sequence given the model. In order to increase the convergence rate and parameters optimization, we combine iterative procedure with GA. The probability of observation sequence of the train model using iterative procedure, GA, and GA with iterative procedure will be compared along with their convergence rates. The test patterns are chain code sequences generated from 38 isolated on-line Thai handwritten characters. The recognition rate and the probability of the train observation sequences of GA were better than the iterative training. The recognition rate of HMM with iterative training 95.05%, GA 97.50% and GA with iterative training 98.41% on 3839 patterns.