Ergodicity of Fuzzy Markov Chains Based on Simulation Using Halton Sequences
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Authors
Behrouz Fathi Vajargah
- Department of Statistics, University of Guilan, Rasht, Iran
Maryam Gharehdaghi
- Department of Statistics, University of Guilan, Rasht, Iran
Abstract
We first introduce fuzzy finite Markov chains and present some of their fundamental properties based on possibility theory. We also bring in a way to convert fuzzy Markov chains to classic Markov chains. In addition, we simulate fuzzy Markov chain using different sizes. It is observed that the most of fuzzy Markov chains not only do have an ergodic behavior, but also they are periodic. Finally, using Halton quasi-random sequence we generate some fuzzy Markov chains which compared to the ones generated by the RAND function of MATLAB. Therefore, we improve the periodicity behavior of fuzzy Markov chains.
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ISRP Style
Behrouz Fathi Vajargah, Maryam Gharehdaghi, Ergodicity of Fuzzy Markov Chains Based on Simulation Using Halton Sequences, Journal of Mathematics and Computer Science, 4 (2012), no. 3, 380--385
AMA Style
Fathi Vajargah Behrouz, Gharehdaghi Maryam, Ergodicity of Fuzzy Markov Chains Based on Simulation Using Halton Sequences. J Math Comput SCI-JM. (2012); 4(3):380--385
Chicago/Turabian Style
Fathi Vajargah, Behrouz, Gharehdaghi, Maryam. "Ergodicity of Fuzzy Markov Chains Based on Simulation Using Halton Sequences." Journal of Mathematics and Computer Science, 4, no. 3 (2012): 380--385
Keywords
- Fuzzy Markov Chains
- Stationary Distribution
- Ergodicity
- Simulation
- Halton Quasi-Random Sequence.
MSC
- 60J10
- 60A86
- 60J22
- 65C40
- 15B15
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