Classifying States of a Finite Markov Chain with Membrane Computing

  • Mónica Cardona
  • M. Angels Colomer
  • Mario J. Pérez-Jiménez
  • Alba Zaragoza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4361)


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.


Markov Chain Equivalence Class Turing Machine Priority Relation Boolean Matrix 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mónica Cardona
    • 1
  • M. Angels Colomer
    • 1
  • Mario J. Pérez-Jiménez
    • 2
  • Alba Zaragoza
    • 1
  1. 1.Department of MathematicsUniversity of LleidaLleidaSpain
  2. 2.Research Group on Natural Computing, Department of Computer Science and Artificial IntelligenceUniversity of SevillaSevillaSpain

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