Ergodicity of Fuzzy Markov Chains Based on Simulation Using Sequences


Authors

Behrouz Fathi Vajargah - Department of Statistics, University of Guilan, Rasht, Iran. Maryam Gharehdaghi - Department of Statistics, University of Guilan, Rasht, Iran.


Abstract

As shown in [1], we reduce periodicity of fuzzy Markov chains using the Halton quasi-random generator. In this paper, we employ two different quasi-random sequences namely Faure and Kronecker to generate the membership values of fuzzy Markov chain. Using simulation it is revealed that the number of ergodic fuzzy Markov chain simulated by Kronecker sequences is more than the one obtained by Faure sequences.


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ISRP Style

Behrouz Fathi Vajargah, Maryam Gharehdaghi, Ergodicity of Fuzzy Markov Chains Based on Simulation Using Sequences, Journal of Mathematics and Computer Science, 11 (2014), no. 2, 159 - 165

AMA Style

Vajargah Behrouz Fathi, Gharehdaghi Maryam, Ergodicity of Fuzzy Markov Chains Based on Simulation Using Sequences. J Math Comput SCI-JM. (2014); 11(2):159 - 165

Chicago/Turabian Style

Vajargah, Behrouz Fathi, Gharehdaghi, Maryam. "Ergodicity of Fuzzy Markov Chains Based on Simulation Using Sequences." Journal of Mathematics and Computer Science, 11, no. 2 (2014): 159 - 165


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