On observability of discrete-event systems
Information Sciences: an International Journal - Robotics and Automation/Control Series
Minimization of fuzzy finite automata
Information Sciences: an International Journal
Minimization of states in automata theory based on finite lattice-ordered monoids
Information Sciences: an International Journal
Decision making in fuzzy discrete event systems
Information Sciences: an International Journal
Diagnosability of fuzzy discrete event systems
Information Sciences: an International Journal
Information Sciences: an International Journal
Fuzzy discrete-event systems under fuzzy observability and a test algorithm
IEEE Transactions on Fuzzy Systems
The relationships among several types of fuzzy automata
Information Sciences: an International Journal
Behavior-modulation technique in mobile robotics using fuzzy discrete event system
IEEE Transactions on Robotics
A Fuzzy Discrete Event System Approach to Determining Optimal HIV/AIDS Treatment Regimens
IEEE Transactions on Information Technology in Biomedicine
Modeling and control of fuzzy discrete event systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervisory control of fuzzy discrete event systems: a formal approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Observability and decentralized control of fuzzy discrete-event systems
IEEE Transactions on Fuzzy Systems
Fuzzy automata with fuzzy relief
IEEE Transactions on Fuzzy Systems
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In order to determine uncertainties from restricted available information, fuzzy discrete-event systems (FDESs), or fuzzy discrete-event dynamic systems (FDEDSs), were recently proposed. These frameworks include fuzzy states and events occurring simultaneously with different membership degrees. Fuzzy states and events have been used to describe uncertainties that occur often in practical problems, such as treatment planning for HIV/AIDS patients, sensory information processing for robotic control, and fault diagnosis problems. In order to measure information associated with FDESs or FDEDSs, the classical discrete event system (DES) observability has been turned into fuzzy observability for FDESs or FDEDSs. The newly proposed method allows ease of defining degrees of observability so that uncertainties in FDESs or FDEDSs can be dealt with effectively. This gives an opportunity to design better decision-making systems. To calculate the observability degree, a simple fuzzy observability checking method is introduced, and two examples are elaborated upon to illustrate the presented method. Finally, the newly proposed method is tested on a heating, ventilating, and air-conditioning (HVAC) system.