Capítulo de Libro
Classifying States of a Finite Markov Chain with Membrane Computing
Autor/es | Cardona, Mónica
Colomer, M. Angels Pérez Jiménez, Mario de Jesús Zaragoza, Alba |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2006 |
Fecha de depósito | 2017-02-02 |
Publicado en |
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ISBN/ISSN | 978-3-540-69088-7 0302-9743 |
Resumen | 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 ... 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. |
Agencias financiadoras | Ministerio de Educación y Ciencia (MEC). España Junta de Andalucía |
Identificador del proyecto | TIN2005-09345-C04-01
TIC-581 |
Cita | Cardona, M., Colomer, M.A.,...,Zaragoza, A. (2006). Classifying States of a Finite Markov Chain with Membrane Computing. En Membrane Computing. WMC 2006. Lecture Notes in Computer Science, vol 4361 (pp. 266-278). Berlin: Springer. |
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