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Constraints in Particle Swarm Optimization of Hidden Markov Models

  • Martin Macaš
  • Daniel Novák
  • Lenka Lhotská
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

This paper presents new application of Particle Swarm Optimization (PSO) algorithm for training Hidden Markov Models (HMMs). The problem of finding an optimal set of model parameters is numerical optimization problem constrained by stochastic character of HMM parameters. Constraint handling is carried out using three different ways and the results are compared to Baum-Welch algorithm (BW), commonly used for HMM training. The global searching PSO method is much less sensitive to local extremes and finds better solutions than the local BW algorithm, which often converges to local optima. The advantage of PSO approach was markedly evident, when longer training sequence was used.

Keywords

Particle Swarm Optimization Hide Markov Model Particle Swarm Optimization Algorithm Observation Sequence Constraint Handling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Martin Macaš
    • 1
  • Daniel Novák
    • 1
  • Lenka Lhotská
    • 1
  1. 1.Dep. of CyberneticsCzech Technical University, Faculty of Electrical EngineeringPragueCzech Republic

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