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http://dx.doi.org/10.5370/JEET.2012.7.1.109

Training HMM Structure and Parameters with Genetic Algorithm and Harmony Search Algorithm  

Ko, Kwang-Eun (School of Electrical and Electronics Engineering, Chung-Ang University)
Park, Seung-Min (School of Electrical and Electronics Engineering, Chung-Ang University)
Park, Jun-Heong (School of Electrical and Electronics Engineering, Chung-Ang University)
Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
Publication Information
Journal of Electrical Engineering and Technology / v.7, no.1, 2012 , pp. 109-114 More about this Journal
Abstract
In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.
Keywords
Hidden markov model; Genetic algorithm; Harmony search algorithm; Optimization;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By Web Of Science : 0
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