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

• Autorzy: Bijak K.
• Języki publikacji: EN

Abstrakty

EN

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 1901C1990. Then, on the basis of the estimation results, the most likely sequences of states of the hidden Markov chains are found.

Słowa kluczowe

EN

hidden Markov model (HMM); hybrid HMM/ANN model; Markov chains; neural network; seismic event; genetic algorithm

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2008
• Tom: Vol. 86, nr 1-2
• Strony: 1--17
• Opis fizyczny: bibliogr. 18 poz., tab., wykr.

Twórcy

autor

Bijak K.

• Scoring Department, Credit Information Bureau, Modzelewskiego 77A, 02-679 Warsaw, Poland Address for corr.: Kluczborska 7/8, 01-461 Warsaw, Poland,

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