The University of Southampton
University of Southampton Institutional Repository

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
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
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), 1-17.

Record type: Article

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.

Full text not available from this repository.

More information

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: https://eprints.soton.ac.uk/id/eprint/361313
PURE UUID: 44e7faef-0f8d-4981-8949-7a7d4f19c727

Catalogue record

Date deposited: 22 Jan 2014 11:42
Last modified: 18 Jul 2017 03:04

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×