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Classifying States of a Finite Markov Chain with Membrane Computing

 

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Opened Access Classifying States of a Finite Markov Chain with Membrane Computing
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Author: Cardona, Mónica
Colomer, M. Angels
Pérez Jiménez, Mario de Jesús
Zaragoza, Alba
Department: Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial
Date: 2006
Published in: Membrane Computing. WMC 2006. Lecture Notes in Computer Science, vol 4361
ISBN/ISSN: 978-3-540-69088-7
0302-9743
Document type: Chapter of Book
Abstract: In this paper we present a method to classify the states of a finite Markov chain through membrane computing. A specific P system with external output is designed for each boolean matrix associated with a finite Markov chain. The computation of the system allows us to decide the convergence of the process because it determines in the environment the classification of the states (recurrent, absorbent, and transient) as well as the periods of states. The amount of resources required in the construction is polynomial in the number of states of the Markov chain.
Size: 541.8Kb
Format: PDF

URI: http://hdl.handle.net/11441/53544

DOI: 10.1007/11963516_17

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