Decision Support With Logical and Fuzzy Petri Nets

Lehocki, Fedor; Juhás, Gabriel; Lorenz, Robert; Szczerbicka, Helena; Drozda, Martin

doi:10.1080/01969720802188235

Cited by 7 publications

(15 citation statements)

References 12 publications

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“…Using Logical and Fuzzy Petri Nets, these requirements translate as the reachability of a unique stable marking from the given initial marking by application of a finite sequence of production rules. In [1] we have proved that this requirement is satisfied.…”

Section: Introductionmentioning

confidence: 80%

“…For clarity we also briefly introduce the results from ref. [1] aimed at basic concepts of Logical and Fuzzy Petri Nets and continue with extended Fuzzy Petri Nets utilizing rule thresholds and weights. Several dialects of Logical and Fuzzy Petri Nets are used in the literature [2, 6, 9-11, 13, 15].…”

Section: Introductionmentioning

confidence: 99%

“…Several dialects of Logical and Fuzzy Petri Nets are used in the literature [2, 6, 9-11, 13, 15]. In this paper, we extend the approach presented in [1], which follows the approach given in [11]. Our approach assumes that if a transition produces a token to a place which is already marked by a token, the both tokens in that place are merged to one token.…”

Section: Introductionmentioning

confidence: 99%

“…Adding a token to an already marked place represents just a different proof of validity of an already valid proposition. Another assumption of [1] is that once a proposition is valid, it stays valid after application of a production rule, i.e. once a place of the net is marked by a token, firing of a transition does not consume the token from the place.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Extended Fuzzy Petri Nets for decision support

Lehocki

Juhás

Lorenz

et al. 2008

2008 First International Symposium on Applied Sciences on Biomedical and Communication Technologies

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We present results for the formalism of Extended Fuzzy Petri Nets by enriching the enabling and firing rule by introducing the so-called weights and thresholds in order to filter the propagated knowledge. It is shown that knowledge propagation described using such extended Fuzzy Petri nets still terminates in a unique stable state. Based on this result, the paper introduces an algorithm for knowledge propagation in medical decision support systems. We also discuss the general properties of knowledge propagation functions terminating in a unique stable state leading to the final recommendation of diagnose or treatment.I.

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Section: Introductionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extended Fuzzy Petri Nets for decision support

Lehocki

Juhás

Lorenz

et al. 2008

2008 First International Symposium on Applied Sciences on Biomedical and Communication Technologies

Self Cite

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“…To improve the representation capability of WFPRs, Ha et al [16] introduced two knowledge representation parameters, i.e., the input weight and the output weight, for a fuzzy rule, and defined a GFPN for WFPR reasoning. Lehocki et al [35] proposed a formalism of extended FPNs for decision support systems by enriching the enabling and firing rules via introducing weights and thresholds in order to filter the propagated knowledge.…”

Section: B Fpnsmentioning

confidence: 99%

Fuzzy Petri nets Using Intuitionistic Fuzzy Sets and Ordered Weighted Averaging Operators

Liu

You

et al. 2016

IEEE Trans. Cybern.

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Fuzzy Petri nets (FPNs) are an important modeling tool for knowledge representation and reasoning, which have been extensively used in a lot of fields. However, the conventional FPN models have been criticized as having many shortcomings in the literature. Many different models have been suggested to enhance the performance of FPNs, but deficiencies still exist in these models. First, various types of uncertain knowledge information provided by domain experts are very hard to be modeled by the existing FPN models. Second, the traditional FPNs determine the results of knowledge reasoning using the min, max, and product operators, which may not work well in many practical applications. In this paper, we propose a new type of FPN model based on intuitionistic fuzzy sets and ordered weighted averaging operators to deal with the problems and improve the effectiveness of the conventional FPNs. Moreover, a max-algebra-based reasoning algorithm is developed in order to implement the intuitionistic fuzzy reasoning formally and automatically. Finally, a case study concerning fault diagnosis of aircraft generator is presented to demonstrate the proposed intuitionistic FPN model. Numerical experiments show that the new FPN model is feasible and quite effective for knowledge representation and reasoning of intuitionistic fuzzy expert systems.

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FPNs for Knowledge Representation and Reasoning: A Literature Review

Shi,

Liu

2023

Fuzzy Petri Nets for Knowledge Representation, Acquisition and Reasoning

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Decision Support With Logical and Fuzzy Petri Nets

Cited by 7 publications

References 12 publications

Extended Fuzzy Petri Nets for decision support

Extended Fuzzy Petri Nets for decision support

Fuzzy Petri nets Using Intuitionistic Fuzzy Sets and Ordered Weighted Averaging Operators

FPNs for Knowledge Representation and Reasoning: A Literature Review

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