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Authors: Y. M. Hu 1 ; F. Y. Xie 2 ; B. Wu 1 ; Y. Cheng 1 ; G. F. Jia 3 ; Y. Wang 4 and M. Y. Li 1

Affiliations: 1 Huazhong University of Science and Technology, China ; 2 Huazhong University of Science and Technology and East China Jiaotong University, China ; 3 Huazhong University of Science& Technology, China ; 4 Georgia Institute of Technology, United States

ISBN: 978-989-8565-21-1

ISSN: 2184-2809

Keyword(s): Generalized Hidden Markov Model, Generalized Jensen Inequality, Generalized Baum-Welch Algorithm.

Related Ontology Subjects/Areas/Topics: Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Modeling, Analysis and Control of Discrete-event Systems ; Modeling, Simulation and Architectures ; Optimization Algorithms ; Optimization Problems in Signal Processing ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control ; System Modeling

Abstract: Recently a generalized hidden Markov model (GHMM) was proposed for solving the problems of aleatory uncertainty and epistemic uncertainty in engineering application. In GHMM, the aleraory uncertainty is derived by the probability measure while epistemic uncertainty is modelled by the generalized interval. Given any finite observation sequence as training data, the problem of training GHMM is often encountered. In this paper, an optimization method for training GHMM, as a generalization of Baum-Welch algorithm, is proposed. The generalized convex and concave functions based on the generalized interval are proposed for inferring the generalized Jensen inequality. With generalized Baum-Welch’s auxiliary function and generalized Jensen inequality, similar to the multiple observations training, the GHMM parameters are estimated and updated by the lower and the bound observation sequences. A set of training equations and re-estimated formulas have been derived by optimizing the objective fu nction. Similar to multiple observations (expectation maximization) EM algorithm, this method guarantees the local maximum of the lower and the upper bound and hence the convergence of the GHMM training process. (More)

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Paper citation in several formats:
M. Hu, Y.; Y. Xie, F.; Wu, B.; Cheng, Y.; F. Jia, G.; Wang, Y. and Y. Li, M. (2012). An Optimization Method for Training Generalized Hidden Markov Model based on Generalized Jensen Inequality.In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-21-1, ISSN 2184-2809, pages 268-274. DOI: 10.5220/0004118202680274

@conference{icinco12,
author={Y. M. Hu. and F. Y. Xie. and B. Wu. and Y. Cheng. and G. F. Jia. and Y. Wang. and M. Y. Li.},
title={An Optimization Method for Training Generalized Hidden Markov Model based on Generalized Jensen Inequality},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2012},
pages={268-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004118202680274},
isbn={978-989-8565-21-1},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - An Optimization Method for Training Generalized Hidden Markov Model based on Generalized Jensen Inequality
SN - 978-989-8565-21-1
AU - M. Hu, Y.
AU - Y. Xie, F.
AU - Wu, B.
AU - Cheng, Y.
AU - F. Jia, G.
AU - Wang, Y.
AU - Y. Li, M.
PY - 2012
SP - 268
EP - 274
DO - 10.5220/0004118202680274

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