On the Knowledge-Based Dynamic Fuzzy Sets

Intan, Rolly; Halim, Siana; Dewi, Lily Puspa

doi:10.1007/978-981-13-6861-5_25

Cited by 2 publications

(11 citation statements)

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“…Here, ( ) ∈ ℱ( ) is regarded as a knowledgebased dynamic fuzzy set of which is based on knowledge at the time . ( ) is also a similar concept to the fuzzy set defined by Zadeh in 1965 [7,8], where ℱ( ) is a fuzzy power set of . Set of knowledge-based dynamic fuzzy sets on , denoted by ( ) is given by…”

Section: Knowledge-based Dynamic Fuzzy Sets (Kdfs)mentioning

confidence: 99%

“…As discussed by Intan and Mukaidono [5,6,7] in proposing the concept of knowledge-based fuzzy sets, the granularity of knowledge was constructed to obtain the similarity classes of knowledge. All knowledge in a specific similarity class will consider having a similar perception subjectively toward a given fuzzy set.…”

Section: Granularity Of Knowledgementioning

confidence: 99%

“…Through the similarity classes of knowledge, this paper discusses and introduces three necessary measures, namely Objectivity Measures, Individuality Measures and Consistency Measure in the knowledge-based dynamic fuzzy sets. Here, the similarity classes of knowledge are provided by a fuzzy conditional probability relation [5,6,7] which is an asymmetric relation as defined by Definition 3.…”

Section: Granularity Of Knowledgementioning

confidence: 99%

“…As discussed by [5,6,7], the probability is a concept to solve the problem of objective uncertainty. On the other hands, fuzziness is regarded as a concept to represent a subjective uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…A certain knowledge subjectively determines the membership degree of an element in a given fuzzy set. To realize this situation, [5,6,7] introduced an extended concept of fuzzy sets, called Knowledge-based Fuzzy Sets (KFS). Like the DFS, the KFS may also be considered as multi-fuzzy sets by means that every fuzzy label may have different fuzzy sets represented by different pieces of knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Granularity in the Knowledge-based Dynamic Fuzzy Sets

Intan

Halim

Dewi

2018

Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

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In 2002, Intan and Mukaidono proposed Knowledge-based Fuzzy Sets (KFS) as an extended concept of the fuzzy set. Here, the membership function of a fuzzy set is subjectively determined by the knowledge. Wang et al. (1988) generalized the concept of fuzzy set, called Dynamic Fuzzy Sets (DFS). In the DFS, the membership degree of an element might dynamically change according to the time's variable. Both extended concepts of fuzzy sets were then combined by Intan et al. to be a hybrid concept, called Knowledgebased Dynamic Fuzzy Set. The concept is regarded as a more generalization of fuzzy sets by considering that the membership function of a given fuzzy set provided by a certain knowledge may be dynamically changed over time as usually happened in the realworld application. To continually extend the concept of knowledge-based dynamic fuzzy sets, this paper discusses how the fuzzy granularity is constructed in the knowledge-based dynamic fuzzy sets. The concepts of objectivity and consistency are discussed, along with their proposed measures.

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Section: Knowledge-based Dynamic Fuzzy Sets (Kdfs)mentioning

confidence: 99%

Section: Granularity Of Knowledgementioning

confidence: 99%

Section: Granularity Of Knowledgementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuzzy Granularity in the Knowledge-based Dynamic Fuzzy Sets

Intan

Halim

Dewi

2018

Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

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Approximate Reasoning in the Knowledge-Based Dynamic Fuzzy Sets

Intan¹,

Halim²,

Dewi³

2020

JCP

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Intan and Mukaidono discussed that knowledge plays an important role in determining the membership function of a given fuzzy set by introducing a concept, called Knowledge-based Fuzzy Sets (KFS) in 2002. Here, the membership degree of an element given a fuzzy set is subjectively determined by the knowledge. Every knowledge may have each different membership degree of the element given the fuzzy set. In 1988, Wang et al. extended the concept of fuzzy set, called Dynamic Fuzzy Sets (DFS) by considering that the membership degree of an element given a fuzzy set might be dynamically changeable over the time. Both generalized concepts, KFS and DFS, were hybridized by Intan et al. to be a Knowledge-based Dynamic Fuzzy Set (KDFS). As usually happened in the real-world application, the KDFS showed that a membership function of a given fuzzy set subjectively determined by a certain knowledge may be dynamically changeable over time. Moreover, the concept of fuzzy granularity was discussed dealing with the KDFS. Related to the concept of fuzzy granularity in KDFS, this paper discusses the concept of approximate reasoning of KDFS in representing fuzzy production rules as generally applied in the fuzzy expert system.

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On the Knowledge-Based Dynamic Fuzzy Sets

Abstract: In 1965, L.

Cited by 2 publications

References 11 publications

Fuzzy Granularity in the Knowledge-based Dynamic Fuzzy Sets

Fuzzy Granularity in the Knowledge-based Dynamic Fuzzy Sets

Approximate Reasoning in the Knowledge-Based Dynamic Fuzzy Sets

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