Results 311 to 320 of about 164,645 (346)
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A fuzzy logic inference processor
Third International Conference on Industrial Fuzzy Control and Intelligent Systems, 1994A mixed analog-digital fuzzy logic inference engine chip fabricated in an 0.8 /spl mu/m CMOS process is described. Interface to the processor behaves like a static RAM, and computation of the fuzzy logic inference is performed between memory locations in parallel by an array of analog charge-domain circuits.
John W. Fattaruso +2 more
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Specification and inference of fuzzy attributes
2011 IEEE Symposium on Foundations of Computational Intelligence (FOCI), 2011In this work, an extension of the database relational model which incorporates vague or imprecise data is presented. Specifically, we extend the concept of functional dependency to Fuzzy Attributes Tables. This extension is based on the use of a residuated lattice as a truthfulness value set.
Pablo Cordero +4 more
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A fuzzy inference network for classification
1995 International Conference on Acoustics, Speech, and Signal Processing, 2002A fuzzy inference network (FIN) is proposed. The proposed FIN preserves the advantages of both fuzzy classification algorithm and neural networks. It can learn membership functions directly from training samples and classify patterns according to the membership values. As efficient self-organizing learning algorithm is also presented.
Lynn Yaling Cai, Hon Keung Kwan
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Fuzzy inference networks: an introduction
Proceedings of International Conference on Neural Networks (ICNN'97), 2002One of the problems of existing fuzzy-neural approaches is that the logic nature of the structure is often lost, i.e., what is being processed by the neural networks becomes irrelevant. To retain this logic content while benefiting from the advantage of integrating fuzzy set and neural network approaches, we propose in this paper a fuzzy neural network
Witold Pedrycz, Michael H. Smith
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Credibility in Fuzzy Inference Systems
Cybernetics and Systems Analysis, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Provotar, O. I., Provotar, O. O.
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Algorithms for fuzzy inference and tuning in the fuzzy inference software FINEST
Proceedings of 1995 IEEE International Conference on Fuzzy Systems. The International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, 2002In this paper, we explain the algorithms used in FINEST, the Fuzzy Inference Environment Software with Tuning developed at LIFE (Laboratory for International Fuzzy Engineering Research). The research themes and associated algorithms were defined to palliate the insufficiencies of usual inference methods and come naturally from the formulation of fuzzy "
T. Arnould +6 more
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An algorithmic approach for fuzzy inference
IEEE Transactions on Fuzzy Systems, 1997To apply fuzzy logic, two major tasks need to be performed: the derivation of production rules and the determination of membership functions. These tasks are often difficult and time consuming. This paper presents an algorithmic method for generating membership functions and fuzzy production rules; the method includes an entropy minimization for ...
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Deep combination of fuzzy inference and neural network in fuzzy inference software — FINEST
Fuzzy Sets and Systems, 1996Abstract At the Laboratory for International Fuzzy Engineering Research in Japan (LIFE), we are now developing FINEST (Fuzzy Inference Environment Software with Tuning). The special features are (1) improved generalized modus ponens, (2) mechanism which can tune the inference method as well as fuzzy predicates and (3) software environment for ...
Shun'ichi Tano +2 more
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Fuzzy inferences by algebraic method
Fuzzy Sets and Systems, 1997The author gives a fuzzy measurement theory, which is more general than the measurement theory for classical and quantum systems. This approach is given in terms of \(C^*\)-algebras, proposing the identification ``measurement = inference'' as axiom. Several results are given in terms of fuzzy syllogisms for classical systems.
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Fuzzy inference neural network for fuzzy model tuning
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996In fuzzy modeling, it is relatively easy to manually define rough fuzzy rules for a target system by intuition. It is, however, time-consuming and difficult to fine-tune them to improve their behavior. This paper describes a tuning method for fuzzy models which is applicable regardless of the form of fuzzy rules and the used defuzzification method. For
Keon-Myung Lee +2 more
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