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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Macaš, M., Novák, D., & Lhotská, L. (2006). Constraints in particle swarm optimization of hidden markov models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4224 LNCS, pp. 1399–1406). Springer Verlag. https://doi.org/10.1007/11875581_166
Mendeley helps you to discover research relevant for your work.