Representing generalized fuzzy automata in recurrent neural networks

Doostfatemeh, M.; Kremer, Stefan C.

doi:10.1109/ccece.2004.1347583

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…Accordingly, fuzzy finite automata have been applied in many areas, such as learning systems, the model of computing with words, pattern recognition, lattice-valued fuzzy finite automata, and clinical monitoring, and also used as models of machine learning systems (Ying, 2002;Li and Shi, 2000). In general, fuzzy automata have provided an attractive systematic way of generalizing discrete applications (Cattaneo et al, 1997;Doostfatemeh and Kremer, 2003;Reiter, 2002;Srivastava and Tiwari, 2002). Moreover, fuzzy automata developed capabilities that are hardly achievable by other tools (Ying, 2002).…”

Section: Introductionmentioning

confidence: 99%

Isomorphism Between Two Vector General Fuzzy Automata

Sen¹,

Movahednia²,

Sen³

2023

Informatica

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In this paper, at first, we define the notion of general fuzzy automaton over a field; we call this automaton vector general fuzzy automaton (VGFA). Moreover, we present the concept of max-min vector general fuzzy automaton. We show that if two max-min VGFA are similar, they constitute an isomorphism. After that, we prove that if two VGFA constitute an isomorphism with threshold α, they are equivalent with threshold α, where $\alpha \in [0,1]$. Also, some examples are given to clarify these new notions.

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Section: Introductionmentioning

confidence: 99%

Isomorphism Between Two Vector General Fuzzy Automata

Sen¹,

Movahednia²,

Sen³

2023

Informatica

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“…Automaton (or more precisely a finite state automaton) are the prime example of general computational systems over discrete spaces and have a long history both in theory and application (Thomas, 1990;Moghari, Zahedi, & Ameri, 2011). A finite state automaton is an appropriate tool for modeling systems and applications which can be realized as finite set of states and transition between them depending on some input strings (Doostfatemeh & Kremer, 2004). And, the behavior of discrete event system modeled by an automaton is described by the language generated by the automaton.…”

Section: Introductionmentioning

confidence: 99%

Learning Diagnosis Based on Evolving Fuzzy Finite State Automaton

2014

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Nowadays, determining faults (or critical situations) in non- stationary environment is a challenging task in complex systems such as Nuclear center, or multi-collaboration such as crisis management. A discrete event system or a fuzzy discrete event system approach with a fuzzy role-base may re- solve the ambiguity in a fault diagnosis problem especially in the case of multiple faults (or multiple critical situations). The main advantage of fuzzy finite state automaton is that their fuzziness allows them to handle imprecise and uncertain data, which is inherent to real-world phenomena, in the form of fuzzy states and transitions. Thus, most of approaches proposed for fault diagnosis of discrete event systems require a complete and accurate model of the system to be diagnosed. However, in non-stationary environment it is hard or impossible to obtain the complete model of the system. The focus of this work is to propose an evolving fuzzy discrete event system whose an activate degree is associated to each active state and to develop a fuzzy learning diagnosis for incomplete model. Our approach use the fuzzy set of output events of the model as input events of the diagnoser and the output of a fuzzy system should be defuzzified in an appropriate way to be usable by the environment.

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Biological Sequence Prediction using General Fuzzy Automata

Doostfatemeh

Kremer²

2005

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

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Representing generalized fuzzy automata in recurrent neural networks

Cited by 7 publications

References 6 publications

Isomorphism Between Two Vector General Fuzzy Automata

Isomorphism Between Two Vector General Fuzzy Automata

Learning Diagnosis Based on Evolving Fuzzy Finite State Automaton

Biological Sequence Prediction using General Fuzzy Automata

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