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

摘要

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 1901-1990. Then, on the basis of the estimation results, the most likely sequences of states of the hidden Markov chains are found.