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54
The Paradoxical Success of Fuzzy Logic
 IEEE Expert
, 1993
"... Applications of fuzzy logic in heuristic control have been highly successful, but which aspects of fuzzy logic are essential to its practical usefulness? This paper shows that an apparently reasonable version of fuzzy logic collapses mathematically to twovalued logic. Moreover, there are few if any ..."
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Cited by 89 (1 self)
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Applications of fuzzy logic in heuristic control have been highly successful, but which aspects of fuzzy logic are essential to its practical usefulness? This paper shows that an apparently reasonable version of fuzzy logic collapses mathematically to twovalued logic. Moreover, there are few if any published reports of expert systems in realworld use that reason about uncertainty using fuzzy logic. It appears that the limitations of fuzzy logic have not been detrimental in control applications because current fuzzy controllers are far simpler than other knowledgebased systems. In the future, the technical limitations of fuzzy logic can be expected to become important in practice, and work on fuzzy controllers will also encounter several problems of scale already known for other knowledgebased systems. 1
Comparison of fuzzy numbers using a fuzzy distance measure
, 2002
"... A new approach for ranking fuzzy numbers based on a distance measure is introduced. A new class of distance measures for interval numbers that takes into account all the points in both intervals is developed rst, and then it is used to formulate the distance measure for fuzzy numbers. The approach i ..."
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Cited by 26 (2 self)
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A new approach for ranking fuzzy numbers based on a distance measure is introduced. A new class of distance measures for interval numbers that takes into account all the points in both intervals is developed rst, and then it is used to formulate the distance measure for fuzzy numbers. The approach is illustrated by numerical examples, showing that it overcomes several shortcomings such as the indiscriminative and counterintuitive behavior of several existing fuzzy ranking approaches.
Basic concepts for a theory of evaluation: the aggregative operator
 European J. Oper. Res
, 1982
"... Starting with an explication of the ”aggregative” concept and deducing a general structure which satisfies a number of minimal requirements (properties of clustering) the main features of a new mathematical theory called ”theory of evaluation ” are developed. The theory sheds new light on such w ..."
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Cited by 25 (1 self)
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Starting with an explication of the ”aggregative” concept and deducing a general structure which satisfies a number of minimal requirements (properties of clustering) the main features of a new mathematical theory called ”theory of evaluation ” are developed. The theory sheds new light on such wellknown concepts as membership, conjunction and disjunction and seems to be very promising tool to handle representation problems as they grow from the fields of theory of fuzzy set, and its many applications, of human decision making and multicriteria analysis. 1
Active and dynamic information fusion for multisensor systems with dynamic bayesian networks
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, PART B,
, 2006
"... Many information fusion applications are often characterized by a high degree of complexity because: 1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; 2) decisions must be made efficiently; and 3) the world situation evolves over time. To addr ..."
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Cited by 22 (2 self)
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Many information fusion applications are often characterized by a high degree of complexity because: 1) data are often acquired from sensors of different modalities and with different degrees of uncertainty; 2) decisions must be made efficiently; and 3) the world situation evolves over time. To address these issues, we propose an information fusion framework based on dynamic Bayesian networks to provide active, dynamic, purposive and sufficing information fusion in order to arrive at a reliable conclusion with reasonable time and limited resources. The proposed framework is suited to applications where the decision must be made efficiently from dynamically available information of diverse and disparate sources.
Preference modelling
 State of the Art in Multiple Criteria Decision Analysis
, 2005
"... This paper provides the reader with a presentation of preference modelling fundamental notions as well as some recent results in this field. Preference modelling is an inevitable step in a variety of fields: economy, sociology, psychology, mathematical programming, even medicine, archaeology, and ob ..."
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Cited by 18 (1 self)
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This paper provides the reader with a presentation of preference modelling fundamental notions as well as some recent results in this field. Preference modelling is an inevitable step in a variety of fields: economy, sociology, psychology, mathematical programming, even medicine, archaeology, and obviously decision analysis. Our notation and some basic definitions, such as those of binary relation, properties and ordered sets, are presented at the beginning of the paper. We start by discussing different reasons for constructing a model or preference. We then go through a number of issues that influence the construction of preference models. Different formalisations besides classical logic such as fuzzy sets and nonclassical logics become necessary. We then present different types of preference structures reflecting the behavior of a decisionmaker: classical, extended and valued ones. It is relevant to have a numerical representation of preferences: functional representations, value functions. The concepts of thresholds and minimal representation are also introduced in this section. In section 7, we briefly explore the concept of deontic logic (logic of preference) and other formalisms associated with &quot;compact representation of preferences &quot; introduced for special purposes. We end the paper with some concluding remarks.
Application of Simulated Annealing Fuzzy Model Tuning to Umbilical Cord AcidBase Interpretation
 IEEE TRANSACTIONS FUZZY SYSTEMS
, 1999
"... Fuzzy logic and fuzzy set theory provide an important framework for representing and managing imprecision and uncertainty in medical expert systems, but the need remains to optimise such systems to enhance performance. This paper presents a general technique for optimizing fuzzy models in fuzzy expe ..."
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Cited by 15 (11 self)
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Fuzzy logic and fuzzy set theory provide an important framework for representing and managing imprecision and uncertainty in medical expert systems, but the need remains to optimise such systems to enhance performance. This paper presents a general technique for optimizing fuzzy models in fuzzy expert systems by simulated annealing and Ndimensional hill climbing simplex method. The application of the technique to a fuzzy expert system for the interpretation of the acidbase balance of blood in the umbilical cord of new born infants is presented. The Spearman Rank Order Correlation statistic was used to assess and to compare the performance of a commercially available crisp expert system, an initial fuzzy expert system and a tuned fuzzy expert system with experienced clinicians. Results showed
`Fuzzy' vs `Nonfuzzy' in Combining Classifiers Designed by Boosting
"... Boosting is recognized as one of the most successful techniques for generating classifier ensembles. Typically, the classifier outputs are combined by the weighted majority vote. The purpose of this study is to demonstrate the advantages of some fuzzy combination methods for ensembles of classifiers ..."
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Cited by 13 (0 self)
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Boosting is recognized as one of the most successful techniques for generating classifier ensembles. Typically, the classifier outputs are combined by the weighted majority vote. The purpose of this study is to demonstrate the advantages of some fuzzy combination methods for ensembles of classifiers designed by Boosting. We ran 2fold crossvalidation experiments on 6 benchmark data sets to compare the fuzzy and nonfuzzy combination methods. On the "fuzzy side" we used the fuzzy integral and the decision templates with different similarity measures. On the "nonfuzzy side" we tried simple combiners such as the majority vote, minimum, maximum, average, product, and the Naive Bayes combination. Surprisingly, the minimum, maximum, average and product, which have been reported elsewhere to work very well on a variety of problems, appeared to be inadequate for our task. Thus the real contest was among the fuzzy combination methods on the one hand, and the weighted majority vote, the simple majority vote, and the Naive Bayes combiner, on the other hand. In our experiments, the fuzzy methods performed consistently better than the nonfuzzy methods. The weighted majority vote showed a stable performance, though slightly inferior to the performance of the fuzzy combiners. The majority vote and the Naive Bayes combiners had erratic behavior, ranging from the best to the worst contestants for different data sets.
MeasurementTheoretic Justification of Connectives in Fuzzy Set Theory
, 1995
"... The problem of representing intersection and union in fuzzy set theory is considered. There are various proposals in the literature to model these concepts. The possibility of using continuous triangular norms and conorms (including min and max) are taken up in a measurementtheoretic setting. T ..."
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Cited by 7 (0 self)
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The problem of representing intersection and union in fuzzy set theory is considered. There are various proposals in the literature to model these concepts. The possibility of using continuous triangular norms and conorms (including min and max) are taken up in a measurementtheoretic setting. The conditions are laid out to arrive at cardinal scales on which addition and multiplication are meaningful and critically discussed. These conditions must either be accepted on normative grounds or must be empirically verified before the modeling process in order to see which operations are meaningful. It is emphasized that the Archimedean axiom and the existence of natural bounds are crucial in arriving at ratio and absolute scale representations. Keywords: Membership functions, measurement theory, operators, relations. 1 Introduction and Preview When Zadeh [45] introduced the concept of a fuzzy set he suggested to use the functions min and max to model set theoretic intersection an...
Intelligent Techniques for Handling Uncertainty in the Assessment of Neonatal Outcome
, 1997
"... a rulebased expert system. This expert system checks results to ensure their consistency, identifies whether the results come from arterial or venous vessels, and then produces an interpretation of their meaning. This `crisp' expert system was validated, verified and commercially released, and ..."
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Cited by 7 (6 self)
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a rulebased expert system. This expert system checks results to ensure their consistency, identifies whether the results come from arterial or venous vessels, and then produces an interpretation of their meaning. This `crisp' expert system was validated, verified and commercially released, and has since been installed at twenty two hospitals all around the United Kingdom. The assessment of umbilical acidbase status is characterised by uncertainty in both the basic data and the knowledge required for its interpretation. Fuzzy logic provides a technique for representing both these forms of uncertainty in a single framework. A `preliminary' fuzzylogic based expert system to interpret errorfree results was developed, based on the knowledge embedded in the crisp expert system. Its performance was compared against clinicians in a validation test, but initially its performance was found to be poor in comparison with the clinicians and inferior to the crisp expert system. An automatic tuni
The Weighting Issue in Fuzzy Logic
 Informatica: An International Journal of Computing and Informatica
, 1997
"... this paper is to establish a class of models for solving the weight problem in fuzzy logic. First, some constraints that weighted fuzzy logic should satisfy are given based on Triangular norms and conorms. Then, based on these constraints, a class of models (referred to as relative weighted models) ..."
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Cited by 6 (2 self)
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this paper is to establish a class of models for solving the weight problem in fuzzy logic. First, some constraints that weighted fuzzy logic should satisfy are given based on Triangular norms and conorms. Then, based on these constraints, a class of models (referred to as relative weighted models) are established for handling weights in fuzzy logic. These models are novel in three aspects: (1) they include nonweighted models as their special cases, (2) the weighted conjunction and weighted disjunction can be distinguished from each other, and (3) the information from all subpropositions can be sufficiently considered. In addition, this paper proposes principles for selecting proper weighted models. 1 Introduction