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Perspectives of fuzzy systems and control
 Fuzzy Sets Syst
, 2005
"... Although fuzzy control was initially introduced as a modelfree control design method based on the knowledge of a human operator, current research is almost exclusively devoted to modelbased fuzzy control methods that can guarantee stability and robustness of the closedloop system. Stateofthear ..."
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Although fuzzy control was initially introduced as a modelfree control design method based on the knowledge of a human operator, current research is almost exclusively devoted to modelbased fuzzy control methods that can guarantee stability and robustness of the closedloop system. Stateoftheart techniques for identifying fuzzy models and designing modelbased controllers are reviewed in this article. Attention is also paid to the role of fuzzy systems in higher levels of the control hierarchy, such as expert control, supervision and diagnostic systems. Open issues are highlighted and an attempt is made to give some directions for future research.
An Intelligent diagnosis method for rotating machinery using least squares mapping and a fuzzy neural network
 Sensors 2012
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MAFMA: multiattribute failure mode analysis
 International Journal of Quality and Reliability Management
, 2000
"... attribute failure mode analysis ..."
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ON THE CONNECTION BETWEEN PROBABILITY BOXES AND POSSIBILITY MEASURES
"... ABSTRACT. We explore the relationship between possibility measures (supremum preserving normed measures) and pboxes (pairs of cumulative distribution functions) on totally preordered spaces, extending earlier work in this direction by De Cooman and Aeyels, among others. We start by demonstrating th ..."
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ABSTRACT. We explore the relationship between possibility measures (supremum preserving normed measures) and pboxes (pairs of cumulative distribution functions) on totally preordered spaces, extending earlier work in this direction by De Cooman and Aeyels, among others. We start by demonstrating that only those pboxes who have 0–1valued lower or upper cumulative distribution function can be possibility measures, and we derive expressions for their natural extension in this case. Next, we establish necessary and sufficient conditions for a pbox to be a possibility measure. Finally, we show that almost every possibility measure can be modelled by a pbox, simply by ordering elements by increasing possibility. Whence, any techniques for pboxes can be readily applied to possibility measures. We demonstrate this by deriving joint possibility measures from marginals, under varying assumptions of independence, using a technique known for pboxes. Doing so, we arrive at a new rule of combination for possibility measures, for the independent case. 1.
XFL: A Language for the Definition of Fuzzy Systems
 Proc. 6th IEEE Int. Conf. on Fuzzy Systems (FUZZIEEE’97
, 1997
"... ..."
Hybrid possibilistic networks
 International Journal of Approximate Reasoning
"... Possibilistic networks are important tools for dealing with uncertain pieces of information. For multiplyconnected networks, it is well known that the inference process is a hard problem. This paper studies a new representation of possibilistic networks, called hybrid possibilistic networks. The ..."
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Possibilistic networks are important tools for dealing with uncertain pieces of information. For multiplyconnected networks, it is well known that the inference process is a hard problem. This paper studies a new representation of possibilistic networks, called hybrid possibilistic networks. The uncertainty is no longer represented by local conditional possibility distributions, but by their compact representations which are possibilistic knowledge bases. We show that the inference algorithm in hybrid networks is strictly more efficient than the ones of standard propagation algorithm.
Stochastic dominance with imprecise information
 Computational Statistics and Data Analysis
"... Stochastic dominance, which is based on the comparison of distribution functions, is one of the most popular preference measures. However, its use is limited to the case where the goal is to compare pairs of distribution functions, whereas in many cases it is interesting to compare sets of distribu ..."
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Stochastic dominance, which is based on the comparison of distribution functions, is one of the most popular preference measures. However, its use is limited to the case where the goal is to compare pairs of distribution functions, whereas in many cases it is interesting to compare sets of distribution functions: this may be the case for instance when the available information does not allow to fully elicitate the probability distributions of the random variables. To deal with these situations, a number of generalisations of the notion of stochastic dominance are proposed; their connection with an equivalent pbox representation of the sets of distribution functions is studied; a number of particular cases, such as sets of distributions associated to possibility measures, are investigated; and an application to the comparison of the Lorenz curves of countries within the same region is presented.
Soundly managing uncertain decisions in diagnostic analysis
"... Abstract. This paper presents two diagnostic methods, which are able to handle large scale distributed industrial plants. It presents logical approaches providing diagnoses in presence of doubts in detection test results. Two fuzzy logical based approaches are proposed in order to soundly take into ..."
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Abstract. This paper presents two diagnostic methods, which are able to handle large scale distributed industrial plants. It presents logical approaches providing diagnoses in presence of doubts in detection test results. Two fuzzy logical based approaches are proposed in order to soundly take into account doubts in the decisions provided by detection tests. The first one relies on a structural analysis introduced by the FDI community and the second one relies on logical analysis based on the IA approach of diagnosis. These techniques have been implemented in the EC project named MAGIC.
Possibility Theory and its Applications: Where Do we Stand?
, 2011
"... This paper provides an overview of possibility theory, emphasizing its historical roots and its recent developments. Possibility theory lies at the crossroads between fuzzy sets, probability and nonmonotonic reasoning. Possibility theory can be cast either in an ordinal or in a numerical setting. Q ..."
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This paper provides an overview of possibility theory, emphasizing its historical roots and its recent developments. Possibility theory lies at the crossroads between fuzzy sets, probability and nonmonotonic reasoning. Possibility theory can be cast either in an ordinal or in a numerical setting. Qualitative possibility theory is closely related to belief revision theory, and commonsense reasoning with exceptiontainted knowledge in Artificial Intelligence. Possibilistic logic provides a rich representation setting, which enables the handling of lower bounds of possibility theory measures, while remaining close to classical logic. Qualitative possibility theory has been axiomatically justified in a decisiontheoretic framework in the style of Savage, thus providing a foundation for qualitative decision theory. Quantitative possibility theory is the simplest framework for statistical reasoning with imprecise probabilities. As such it has close connections with random set theory and confidence intervals, and can provide a tool for uncertainty propagation with limited statistical or subjective information. 1