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
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
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.
Hidden Markov Models, hybrid HMM/ANN models, Markov chains, neural networks, genetic algorithms, seismic events
1-17
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
3 November 2008
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
Bijak, Katarzyna
(2008)
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.
Fundamenta Informaticae, 86 (1-2), .
Abstract
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.
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Published date: 3 November 2008
Keywords:
Hidden Markov Models, hybrid HMM/ANN models, Markov chains, neural networks, genetic algorithms, seismic events
Organisations:
Centre of Excellence for International Banking, Finance & Accounting
Identifiers
Local EPrints ID: 361313
URI: http://eprints.soton.ac.uk/id/eprint/361313
PURE UUID: 44e7faef-0f8d-4981-8949-7a7d4f19c727
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Date deposited: 22 Jan 2014 11:42
Last modified: 10 Jan 2022 02:56
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