Ergodicity of Fuzzy Markov Chains Based on Simulation Using Sequences
Behrouz Fathi Vajargah, Maryam Gharehdaghi
TL;DR
This paper investigates the ergodicity of fuzzy Markov chains generated via different quasi-random sequences, demonstrating that Kronecker sequences produce more ergodic chains than Faure sequences through simulation.
Contribution
It introduces the use of Faure and Kronecker quasi-random sequences to generate fuzzy Markov chains and compares their effectiveness in achieving ergodicity.
Findings
Kronecker sequences yield more ergodic fuzzy Markov chains than Faure sequences.
Simulation results support the effectiveness of Kronecker sequences in this context.
Reduction of periodicity in fuzzy Markov chains is achieved using quasi-random generators.
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks and Applications · Fuzzy Logic and Control Systems · Chaos-based Image/Signal Encryption