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  Genetic Algorithms as an Alternative Method of Parameter Estimation and Finding Most Likely Sequences of States of Hidden Markov Chains for HMMs and Hybrid HMM/ANN Models
 
 
Titel: Genetic Algorithms as an Alternative Method of Parameter Estimation and Finding Most Likely Sequences of States of Hidden Markov Chains for HMMs and Hybrid HMM/ANN Models
Auteur: Bijak, Katarzyna
Verschenen in: Fundamenta informaticae
Paginering: Jaargang 86 (2008) nr. 1-2 pagina's 1-17
Jaar: 2008-11-03
Inhoud: In this paper genetic algorithms are used in estimation and decoding processes of a Hidden Markov Model (HMM) and a hybrid HMM/ANN model with conditional binomial distributions. The hybrid model combines a hidden Markov chain with a perceptron which is assumed to constitute a match network. Genetic algorithms are applied here instead of the traditional methods such as the EM algorithm and the Viterbi algorithm. The paper demonstrates performance of an HMM and a hybrid model in modeling the annual number of months, in which some seismic events are recorded. Parameters of the discrete-time two-state models are estimated using the maximum likelihood method, on the basis of data on seismic events that were recorded in Romania in years 1901┬ĘC1990. Then, on the basis of the estimation results, the most likely sequences of states of the hidden Markov chains are found.
Uitgever: IOS Press
Bronbestand: Elektronische Wetenschappelijke Tijdschriften
 
 

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