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Showing papers on "Fuzzy logic published in 1969"


Journal ArticleDOI
TL;DR: Fuzzy logic deals with propositions which may be ascribed values between falsehood and truth subjectively in either a continuous or a discrete fashion.
Abstract: Fuzzy logic deals with propositions which may be ascribed values between falsehood and truth (0 and 1) subjectively in either a continuous or a discrete fashion. This is in contrast to ordinary logic (two-valued or k-valued logic) in which a given proposition is ascribed values objectively using either deterministic or probabilistic approaches.

108 citations


Journal ArticleDOI
TL;DR: An integrated fuzzy approach for selecting a marketing strategy where fuzzy concepts are used for decision-makers’ subjective judgments to reflect the vague nature of the selection process is presented.
Abstract: This paper presents an integrated fuzzy approach for selecting a marketing strategy. In the integrated approach, fuzzy concepts are used for decision-makers’ subjective judgments to reflect the vague nature of the selection process. Fuzzy AHP and VIKOR are included in the integrated approach. Fuzzy AHP is used to determine the fuzzy weights of criteria and sub-criteria because it can effectively determine various criteria’s weights in a hierarchical structure. VIKOR aims to rank strategies with respect to the sub-criteria. We apply the integrated approach in real case to demonstrate the application of the proposed method.

42 citations


Journal Article
TL;DR: The experimentation results show that the proposed prediction algorithm outperforms existing approaches by achieving better accuracy, specificity, and sensitivity.
Abstract: Over the past decade Heart and diabetes disease prediction are major research works in the past decade. For prediction of the Heart and Diabetes diseases, a model using an approach based on rough sets for reducing the attributes and for classification, fuzzy logic system is proposed in this paper. The overall process of prediction is split into two main steps, 1) Using rough set theory and hybrid firefly and BAT algorithms, feature reduction is done 2) Fuzzy logic system classifies the disease datasets. Reduction of attributes is carried out by rough sets and Hybrid BAT and Firefly optimization algorithm. Then the classification of datasets is carried out by the fuzzy system which is based on the membership function and fuzzy rules. The experimentation is performed on several heart disease datasets available in UCI Machine learning repository like datasets of Hungarian, Cleveland, and Switzerland and diabetes dataset collected from a hospital in India. The experimentation results show that the proposed prediction algorithm outperforms existing approaches by achieving better accuracy, specificity, and sensitivity.

16 citations


Journal Article
TL;DR: The proposed algorithm has been applied in a decision making problem with the help of a numerical example and it is demonstrated that the proposed algorithm efficiently encounters the dimension reduction.
Abstract: Dimensionality reduction plays an effective role in downsizing the data having irregular factors and acquires an arrangement of important factors in the information. Sometimes, most of the attributes in the information are found to be correlated and hence redundant. The process of dimensionality reduction has a wider applicability in dealing with the decision making problems where a large number of factors are involved. To take care of the impreciseness in the decision making factors in terms of the Pythagorean fuzzy information which is in the form of soft matrix. The perception of the information has the parameters - degree of membership, degree of indeterminacy (neutral) and degree of nonmembership, for a broader coverage of the information. We first provided a technique for finding a threshold element and value for the information provided in the form of Pythagorean fuzzy soft matrix. Further, the proposed definitions of the object-oriented Pythagorean fuzzy soft matrix and the parameter-oriented Pythagorean fuzzy soft matrix have been utilized to outline an algorithm for the dimensionality reduction in the process of decision making. The proposed algorithm has been applied in a decision making problem with the help of a numerical example. A comparative analysis in contrast with the existing methodologies has also been presented with comparative remarks and additional advantages. The example clearly validates the contribution and demonstrates that the proposed algorithm efficiently encounters the dimension reduction. The proposed dimensionality reduction technique may further be applied in enhancing the performance of large scale image retrieval.

5 citations


01 Jan 1969
TL;DR: Fuzzy logic deals withpositions which may beribed values between falsehood andtruth subjectively in either acontinuous oradiscrete fashion in either deterministic or probabilistic approaches.
Abstract: Fuzzy logic deals withpropositions which may beas- cribed values between falsehood andtruth (0and1)subjectively in either acontinuous oradiscrete fashion. Thisisincontrast toordi- narylogic (two-valued ork-valued logic) inwhich a given proposition isascribed values objectively using either deterministic or probabi- listic approaches. Ananalysis andsynthesis offuzzy logic functions ispresented and their electronic implementation isdiscussed insome detail. Sugges- tions forpossible uses offuzzylogic inquality control, industrial processes,component testing, andpattern recognition andclassifica- tion areoffered. IndexTerms-Fuzzy logic, fuzzy sets, fuzzy systems, multivalued logic.