Classifying states of a finite markov chain with membrane computing

Authors:
Mónica Cardona;M. Angels Colomer;Mario J. Pérez-Jiménez;Alba Zaragoza
Affiliations:
Department of Mathematics, University of Lleida, Lleida, Spain;Department of Mathematics, University of Lleida, Lleida, Spain;Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, University of Sevilla, Sevilla, Spain;Department of Mathematics, University of Lleida, Lleida, Spain
Venue:
WMC'06 Proceedings of the 7th international conference on Membrane Computing
Year:
2006

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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.