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A proposal for an intuitionistic fuzzy inference system
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016This work describes a method to construct type-1 intuitionistic fuzzy inference systems. This type of systems is able to handle more uncertainty than a type-1 fuzzy inference system and performs faster than a type-2 fuzzy inference system. The concepts of intuitionistic membership, and intuitionistic center of area are proposed, in order to implement a
Amaury Hernandez-Aguila +2 more
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2019
The previous two chapters explained the core concepts related to Fuzzy Logic. They discussed Fuzzy Sets and how they are different from the classical/crisp sets. You also learned about various operations that can be done on them and their properties. Then you learned about membership functions, which define the membership values of each element present
Himanshu Singh, Yunis Ahmad Lone
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The previous two chapters explained the core concepts related to Fuzzy Logic. They discussed Fuzzy Sets and how they are different from the classical/crisp sets. You also learned about various operations that can be done on them and their properties. Then you learned about membership functions, which define the membership values of each element present
Himanshu Singh, Yunis Ahmad Lone
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Generalized Flexible Fuzzy Inference Systems
2013 12th International Conference on Machine Learning and Applications, 2013In this paper, we propose a new variant for incremental, evolving fuzzy systems extraction from data data streams, termed as GEN-FLEXFIS (short for Generalized Flexible Fuzzy Inference Systems). It builds upon the FLEXFIS methodology (published by the authors before) and extends it for generalized Takagi-Sugeno (TS) fuzzy systems, which implement ...
Edwin Lughofer +2 more
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Fuzzy Inference System Based on Fuzzy Associative Memory
Journal of Intelligent & Fuzzy Systems, 1997Afuzzy rule-based system, widely used in the areas of control and pattern recognition, is mostly structured into inference mechanism and fuzzy rules expressed in terms of fuzzy sets. In this paper, we provide a general framework for fuzzy inference, which consists of a learning part and an inferring part.
Dae-Sik Jang, Hyung-Il Choi
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Dynamic Neural Fuzzy Inference System
2009This paper proposes an extension to the original offline version of DENFIS. The new algorithm, DyNFIS, replaces original triangular membership function with Gaussian membership function and use back-propagation to further optimizes the model. Fuzzy rules are created for each clustering centre based on the clustering outcome of evolving clustering ...
Yuan-Chun Hwang, Qun Song 0002
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Some Fuzzy Inference Processes in Picture Fuzzy Systems
2019 11th International Conference on Knowledge and Systems Engineering (KSE), 2019Dealing with uncertain and linguistic information has been always a big problem in the areas of computational intelligence and artificial intelligence. Fuzzy inference mechanism is one of the common approaches to handling uncertain and linguistic information.
Bui Cong Cuong +3 more
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An inference system based on fuzzy logic
Journal of Medical Engineering & Technology, 1998We present the use of fuzzy set theory for the management of imprecision and uncertainty. We first introduced fuzzy set theory according to two different perspectives: the logical and the possibilistic/probabilistic point of view. In addition, several examples of fuzzy sets in different contexts have been considered.
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Fuzzy Inference System for Multiuser Detection in CDMA Systems
IEICE Transactions on Communications, 2006In this letter, multi user detection process in Code Division Multiple Access (CDMA) is performed by fuzzy inference system (FIS) and the bit error rate (BER) performance was compared with the single user bound, the matched filter receiver and neural network receiver.
IŞIK, Yalçın, Taspinar, Necmi
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Diagnosis of Arthritis Through Fuzzy Inference System
Journal of Medical Systems, 2010Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion.
Sachidanand Singh +3 more
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Sparse distributed fuzzy inference systems
Soft Computing, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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