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Janusz Kacprzyk

Bio: Janusz Kacprzyk is an academic researcher from Polish Academy of Sciences. The author has contributed to research in topics: Fuzzy logic & Fuzzy set. The author has an hindex of 47, co-authored 185 publications receiving 13564 citations. Previous affiliations of Janusz Kacprzyk include Systems Research Institute & University of Alberta.


Papers
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Journal ArticleDOI
TL;DR: It is shown that all three parameters describing intuitionistic fuzzy sets should be taken into account while calculating those distances between intuitionistically fuzzy sets.

1,379 citations

Book
01 Oct 1997
TL;DR: This volume is the first in the literature on the increasingly popular Ordered Weighted Averaging (OWA) operators, making it possible to change the form of aggregation from the `pessimistic' minimum-type aggregation through all intermediate types, to the `optimistic' maximum-type aggregations.
Abstract: This volume is the first in the literature on the increasingly popular Ordered Weighted Averaging (OWA) operators. These OWA operators make it possible to change the form of aggregation from the `pessimistic' minimum-type aggregation through all intermediate types including the conventional arithmetic mean and nonconventional aggregations, to the `optimistic' maximum-type aggregations. Included are contributions from a number of fields where these operators have been applied. These fields are decision analysis under uncertainty, learning and classification, multi-person decision-making and consensus formation, and flexible database querying and information retrieval.

936 citations

Journal ArticleDOI
TL;DR: It is shown that the proposed measure can be defined in terms of the ratio of intuitionistic fuzzy cardinalities: of F ∩ F c and F ∪ F c .

829 citations

Book
01 Jan 1994
TL;DR: The Dempster-Shafer Theory of Evidence is applied as a guide for the management of uncertainty in knowledge-based systems.
Abstract: Partial table of contents: DEMPSTER-SHAFER THEORY OF EVIDENCE: GENERAL ISSUES. Measures of Uncertainty in the Dempster-Shafer Theory of Evidence (G. Klir). Comparative Beliefs (S. Wong, et al.). Calculus with Linguistic Probabilities and Beliefs (M. Lamata & S. Moral). FUZZIFICATION OF DEMPSTER-SHAFER THEORY OF EVIDENCE. Rough Membership Functions (Z. Pawlak & A. Skowron). DEMPSTER-SHAFER THEORY IN DECISION MAKING AND OPTIMIZATION. Decision Analysis Using Belief Functions (T. Strat). Interval Probabilities Induced by Decision Problems (T. Whalen). DEMPSTER-SHAFER THEORY FOR THE MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS. Using Dempster-Shafer's Belief-Function Theory in Expert Systems (P. Shenoy). Nonmonotonic Reasoning with Belief Structures (R. Yager). Index.

792 citations

Book
01 Jul 1992
TL;DR: Partial table of contents:Issues in the MANAGEMENT of UNCERTAINty A Survey of Uncertain and Approximate Inference.
Abstract: Partial table of contents: ISSUES IN THE MANAGEMENT OF UNCERTAINTY A Survey of Uncertain and Approximate Inference (R. Neapolitan) Rough Sets: A New Approach to Vagueness (Z. Pawlak) ASPECTS OF FUZZY LOGIC: THEORY AND IMPLEMENTATIONS LT-Fuzzy Logics (H. Rasiowa & N. Cat Ho) On Fuzzy Intuitionistic Logic (E. Turunen) On Modifier Logic (J. Mattila) FUZZY LOGIC FOR APPROXIMATE REASONING Presumption, Prejudice, and Regularity in Fuzzy Material Implication (T. Whalen & B. Schott) Inference for Information Systems Containing Probabilistic and Fuzzy Uncertainties (J. Baldwin) FUZZY LOGIC FOR KNOWLEDGE REPRESENTATION AND ELICITATION Approximate Reasoning in Diagnosis, Therapy, and Prognosis (A. Rocha, et al.) Elementary Learning in a Fuzzy Expert System (J. Buckley) KNOWLEDGE-BASED SYSTEMS USING FUZZY LOGIC Structured Local Fuzzy Logics in MILORD (J. Agustm, et al.) The Validation of Fuzzy Knowledge-Based Systems (A. Chang & L. Hall) FUZZY LOGIC FOR INTELLIGENT DATABASE MANAGEMENT SYSTEMS Fuzzy Querying in Conventional Databases (P. Bosc & O. Pivert) Index.

714 citations


Cited by
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Journal ArticleDOI
TL;DR: It is proved that the envelope of the hesitant fuzzy sets is an intuitionistic fuzzy set, and it is proved also that the operations proposed are consistent with the ones of intuitionist fuzzy sets when applied to the envelope.
Abstract: Several extensions and generalizations of fuzzy sets have been introduced in the literature, for example, Atanassov's intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets. In this paper, we propose hesitant fuzzy sets. Although from a formal point of view, they can be seen as fuzzy multisets, we will show that their interpretation differs from the two existing approaches for fuzzy multisets. Because of this, together with their definition, we also introduce some basic operations. In addition, we also study their relationship with intuitionistic fuzzy sets. We prove that the envelope of the hesitant fuzzy sets is an intuitionistic fuzzy set. We prove also that the operations we propose are consistent with the ones of intuitionistic fuzzy sets when applied to the envelope of the hesitant fuzzy sets. © 2010 Wiley Periodicals, Inc.

2,232 citations

Journal ArticleDOI
Zeshui Xu1
TL;DR: Based on score function and accuracy function, a method is introduced for the comparison between two intuitionistic fuzzy values and some aggregation operators are developed, such as the intuitionism fuzzy weighted averaging operator, intuitionists fuzzy ordered weighted averaging operators, and intuitionistic fuzziness hybrid aggregation operator, for aggregating intuitionist fuzzy values.
Abstract: An intuitionistic fuzzy set, characterized by a membership function and a non-membership function, is a generalization of fuzzy set. In this paper, based on score function and accuracy function, we introduce a method for the comparison between two intuitionistic fuzzy values and then develop some aggregation operators, such as the intuitionistic fuzzy weighted averaging operator, intuitionistic fuzzy ordered weighted averaging operator, and intuitionistic fuzzy hybrid aggregation operator, for aggregating intuitionistic fuzzy values and establish various properties of these operators.

2,131 citations

Journal ArticleDOI
TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.

2,004 citations

Journal ArticleDOI
TL;DR: This paper develops some new geometric aggregation operators, such as the intuitionistic fuzzy weighted geometric (IFWG) operator, the intuitionists fuzzy ordered weighted geometric(IFOWG)operator, and the intuitionism fuzzy hybrid geometric (ifHG) operators, which extend the WG and OWG operators to accommodate the environment in which the given arguments are intuitionistic fuzz sets.
Abstract: The weighted geometric (WG) operator and the ordered weighted geometric (OWG) operator are two common aggregation operators in the field of information fusion. But these two aggregation operators are usually used in situations where the given arguments are expressed as crisp numbers or linguistic values. In this paper, we develop some new geometric aggregation operators, such as the intuitionistic fuzzy weighted geometric (IFWG) operator, the intuitionistic fuzzy ordered weighted geometric (IFOWG) operator, and the intuitionistic fuzzy hybrid geometric (IFHG) operator, which extend the WG and OWG operators to accommodate the environment in which the given arguments are intuitionistic fuzzy sets which are characterized by a membership function and a non-membership function. Some numerical examples are given to illustrate the developed operators. Finally, we give an application of the IFHG operator to multiple attribute decision making based on intuitionistic fuzzy sets.

1,928 citations

Journal ArticleDOI
TL;DR: The concept of a hesitant fuzzy linguistic term set is introduced to provide a linguistic and computational basis to increase the richness of linguistic elicitation based on the fuzzy linguistic approach and the use of context-free grammars by using comparative terms.
Abstract: Dealing with uncertainty is always a challenging problem, and different tools have been proposed to deal with it. Recently, a new model that is based on hesitant fuzzy sets has been presented to manage situations in which experts hesitate between several values to assess an indicator, alternative, variable, etc. Hesitant fuzzy sets suit the modeling of quantitative settings; however, similar situations may occur in qualitative settings so that experts think of several possible linguistic values or richer expressions than a single term for an indicator, alternative, variable, etc. In this paper, the concept of a hesitant fuzzy linguistic term set is introduced to provide a linguistic and computational basis to increase the richness of linguistic elicitation based on the fuzzy linguistic approach and the use of context-free grammars by using comparative terms. Then, a multicriteria linguistic decision-making model is presented in which experts provide their assessments by eliciting linguistic expressions. This decision model manages such linguistic expressions by means of its representation using hesitant fuzzy linguistic term sets.

1,876 citations