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Training HMM Structure and Parameters with Genetic Algorithm and Harmony Search Algorithm
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Training HMM Structure and Parameters with Genetic Algorithm and Harmony Search Algorithm
Ko, Kwang-Eun; Park, Seung-Min; Park, Jun-Heong; Sim, Kwee-Bo;
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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.
Hidden markov model;Genetic algorithm;Harmony search algorithm;Optimization;
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A. Santoso, E. J. Powers and W. M. Grady, "Power Quality Disturbance Identification using Wavelet Transform and Artificial Neural Network," Proc. Int. Conf. Harmonics and Quality of Power, pp.615-618, 1996

J. Chung, E. J. Powers, W. M. Grady, and S. C. Bhatt, "Electric Power Transient Disturbance Classification using Wavelet-based Hidden Markov Models", Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, vol. 6, pp.3662-3665, 2000

W. Min, A. V. Mamishev, "Classification of Power Quality Events using Optimal Time-Frequency Representations-Part 1: Theory," IEEE Transactions on Power Deliver, vol. 19, Issue 3, pp.1488-1495, 2004 crossref(new window)

M. Negnevitsky, M. Ringrose, "A Neuro-Fuzzy System for Recognition of Power Quality Disturbances," IEEE Power Engineering Society General Meeting, vol. 3, pp.2295-2300, 2005

M. C. G. Silveira, A. D. P. Lotufo, C. R. Minussi, "Transient Stability Analysis of Electrical Power Systems using a Neural Network based on Fuzzy ARTMAP," Proc. IEEE Bologna PowerTech Conference, 2003

A.W. Noor Izzri, A. Mohamed, I. Yahya, "A New Method of Transient Stability Assessment in Power Systems using LS-SVM," The 5th Student Conference on Research and Development, 2007

M. Azab, "Harmonic Elimination in Three-Phase Voltage Source Inverters by Particle Swarm Optimization," Journal of Electrical Engineering & Technology, vol. 6, no. 3, pp. 334-341, 2011 crossref(new window)

D. Whitley, "A Genetic Algorithm Tutorial", Statist. Comput., vol. 4, pp. 65-85, 1994

Z. W. Geem, "State-of-the-Art in the Structure of Harmony Search Algorithm," Recent Advances in Harmony Search Algorithm, Studies in Computational Intelligence, vol. 270, pp. 1-10, 2010 crossref(new window)

L. R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proceedings of IEEE, Vol. 77, pp. 257-286, 1989 crossref(new window)

H. K. Lee, S. J. Choi, "PCA+HMM+SVM for EEG pattern classification," Proceedings of seventh international symposium on signal processing and its applications, vol. 1, pp. 541-544, 2003

C. W. Chau, S. Kwong, C. K. Diu, W. R., Fahrner, "Optimization of HMM by a Genetic Algorithm," IEEE Int. Conf. Acoustics, Speech, and Signal Processing, vol. 3, pp. 1727-1730, 1997

Z. W. Geem and K. B. Sim, "Parameter-Setting-Free Harmony Search Algorithm," Applied Mathematics and Computation, vol. 217, no. 8, pp.3881-3889, 2010 crossref(new window)

K. -J. Won, A. Pruger-Bennett and A. Krogh, "Training HMM Structure with Genetic Algorithm for Biological Sequence Analysis," Bioinformatic, vol. 20, no. 18, pp. 3613-3619, 2004 crossref(new window)

D. T. Nguyen and T. A. Hoang, "Analysis of Power Transient Disturbances using Wavelet Transform Modulus Maxima Technique," Proceedings of the Australasian Universities Power Engineering Conference, Brisbane, pp. 190-195, 2000

N. Perera, A. D. Rajapakse, "Power System Transient Classification for Protection Relaying," The 13th Int. Conf. Harmonics and Quality of Power, pp. 1-6, 2008

C. W. Chau, S. Kwong, C. K. Diu and W. R. Fahrner, "Optimization of HMM by a Genetic Algorithm," Proc. ICASSP, pp.1727-1730, 1997

F. Yang and C. Zhang, "An Effective Hybrid Optimization Algorithm for HMM," Proc. Fourth International Conference on Natural Computation, pp.80-84, 2008