Showing papers on "Fuzzy logic published in 1969"
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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
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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
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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
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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.