DOI QR코드

DOI QR Code

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

Ko, Kwang-Eun;Park, Seung-Min;Park, Jun-Heong;Sim, Kwee-Bo

  • 투고 : 2011.06.13
  • 심사 : 2011.08.17
  • 발행 : 2012.01.01

초록

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.

키워드

Hidden markov model;Genetic algorithm;Harmony search algorithm;Optimization

참고문헌

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피인용 문헌

  1. The mechanical arm control based on harmony search genetic algorithm vol.20, pp.4, 2017, https://doi.org/10.5370/JEET.2012.7.1.109
  2. The Reputation Evaluation Based on Optimized Hidden Markov Model in E-Commerce vol.2013, 2013, https://doi.org/10.5370/JEET.2012.7.1.109

과제정보

연구 과제 주관 기관 : NRF