Evolutionary identification of Markov models

Kai Ming Tse; Kah Phooi Seng; Chi Hsu Wang

Evolutionary identification of Markov models

Kai Ming Tse^*, Kah Phooi Seng, Chi Hsu Wang

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Chapter › peer-review

Abstract

A multiresolution dynamic genetic algorithm searching technique is introduced to enhance the on-line model identification for finite-discrete state, discrete-time Markov models with continuous-range observations under white Gaussian noise. Markov chains with 3 states and 4 states are implemented.

Original language	English
Title of host publication	Recent Advances in Computers, Computing and Communications
Publisher	World Scientific and Engineering Academy and Society
Pages	312-315
Number of pages	4
ISBN (Print)	9608052629
Publication status	Published - 2002
Externally published	Yes

Keywords

Evolutionary Algorithms
Markov Chains
Morkov Models
State variables
Transition Probabilities Matrix

Cite this

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title = "Evolutionary identification of Markov models",

abstract = "A multiresolution dynamic genetic algorithm searching technique is introduced to enhance the on-line model identification for finite-discrete state, discrete-time Markov models with continuous-range observations under white Gaussian noise. Markov chains with 3 states and 4 states are implemented.",

keywords = "Evolutionary Algorithms, Markov Chains, Morkov Models, State variables, Transition Probabilities Matrix",

author = "Tse, \{Kai Ming\} and Seng, \{Kah Phooi\} and Wang, \{Chi Hsu\}",

year = "2002",

language = "English",

isbn = "9608052629",

pages = "312--315",

booktitle = "Recent Advances in Computers, Computing and Communications",

publisher = "World Scientific and Engineering Academy and Society",

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T1 - Evolutionary identification of Markov models

AU - Tse, Kai Ming

AU - Seng, Kah Phooi

AU - Wang, Chi Hsu

PY - 2002

Y1 - 2002

N2 - A multiresolution dynamic genetic algorithm searching technique is introduced to enhance the on-line model identification for finite-discrete state, discrete-time Markov models with continuous-range observations under white Gaussian noise. Markov chains with 3 states and 4 states are implemented.

AB - A multiresolution dynamic genetic algorithm searching technique is introduced to enhance the on-line model identification for finite-discrete state, discrete-time Markov models with continuous-range observations under white Gaussian noise. Markov chains with 3 states and 4 states are implemented.

KW - Evolutionary Algorithms

KW - Markov Chains

KW - Morkov Models

KW - State variables

KW - Transition Probabilities Matrix

UR - https://www.scopus.com/pages/publications/4944227189

M3 - Chapter

AN - SCOPUS:4944227189

SN - 9608052629

SP - 312

EP - 315

BT - Recent Advances in Computers, Computing and Communications

PB - World Scientific and Engineering Academy and Society

ER -

Evolutionary identification of Markov models

Abstract

Keywords

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Cite this