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Published on: September 11, 2019
Stochastic Fuzzy Discrete Event Systems and Their Model Identification.
We introduce stochastic fuzzy discrete event systems (SFDESs), a novel framework distinct from probabilistic FDESs. A new technique identifies SFDES parameters without adjustable settings, enabling modeling for diverse applications.
Area of Science:
- Control Systems Engineering
- Fuzzy Logic Systems
- Stochastic Processes
Background:
- Existing probabilistic fuzzy discrete event systems (PFDESs) have limitations.
- Novel stochastic fuzzy discrete event systems (SFDESs) are proposed.
- SFDESs offer a new modeling framework for specific applications.
Purpose of the Study:
- Introduce and define stochastic fuzzy discrete event systems (SFDESs).
- Develop a technique to identify SFDES parameters from scratch.
- Establish conditions for SFDES identification.
Main Methods:
- Focus on single-event SFDESs with one event per fuzzy automaton.
- Develop a prerequired-pre-event-state-based technique for parameter identification.
- Utilize N pre-event state vectors to determine MN^2 unknown parameters.
Main Results:
- A novel technique identifies the number of fuzzy automata, event transition matrices, and occurrence probabilities.
- One necessary and sufficient condition and three sufficient conditions for identification are established.
- The technique requires no adjustable parameters or hyperparameters.
Conclusions:
- SFDESs provide an effective modeling framework for applications unsuitable for PFDESs.
- The proposed identification technique is innovative and parameter-free.
- Numerical examples validate the technique's applicability.
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