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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.
XFL: A Language for the Definition of Fuzzy Systems
 Proc. 6th IEEE Int. Conf. on Fuzzy Systems (FUZZIEEE’97
, 1997
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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.
An Intelligent diagnosis method for rotating machinery using least squares mapping and a fuzzy neural network
 Sensors 2012
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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.
MAFMA: multiattribute failure mode analysis
 International Journal of Quality and Reliability Management
, 2000
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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
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.
Prioritization of Failure Modes in Process FMEA using Fuzzy Logic
"... Abstract: Failure mode and effect analysis (FMEA) is a widely used reliability analysis and risk assessment tool in various industries. It is one of the most established project management techniques to identify and eliminate failures before actual manufacturing starts. In traditional FMEA, Risk Pri ..."
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Abstract: Failure mode and effect analysis (FMEA) is a widely used reliability analysis and risk assessment tool in various industries. It is one of the most established project management techniques to identify and eliminate failures before actual manufacturing starts. In traditional FMEA, Risk Priority Number (RPN) ranking system is used to evaluate; the risk level of failures, to rank failures, and to prioritize actions. Even through this approach is simple but there are some shortcomings in obtaining a good estimate of the failure ratings. Thus, a new risk assessment system based on the fuzzy set theory and fuzzy rule base theory is proposed to deal with these drawbacks. Furthermore, an analysis of a forging industry is presented to demonstrate the traditional FMEA and the proposed FMEA.