18 results with keyword: 'interval type tsk fuzzy inference system'
Given an input or ob- servation, which does not overlap with any rule antecedent, fuzzy interpolation can still approximate the conclusion by considering the neighboring rules in
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This study proposes the use of interval type-2 intuitionistic fuzzy logic system of Takagi-Sugeno-Kang (IT2IFLS-TSK) fuzzy inference that utilises more parameters than type-2
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The Mamdani and Takagi-Sugeno-Kang (TSK) Interval Type-2 Fuzzy Inference Models [10] and the design of Interval Type-2 membership functions and operators are implemented in the
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Adaptive Neuro Fuzzy Inference System (ANFIS) is a fuzzy mapping algorithm that is based on Tagaki-Sugeno-Kang (TSK) fuzzy inference system (Jang et al., 1997
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A novel fuzzy interpolation approach, which extends the TSK inference, is presented in this paper. The proposed fuzzy inference engine is workable with sparse, dense or
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A novel fuzzy interpolation approach, which extends the TSK inference, is presented in this paper. The proposed fuzzy inference engine is workable with sparse, dense or
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JT2FISPanel and JT2FISClusteringPanel are Java visual components to Build Java Intelligent Applications using Java Interval Type-2 Fuzzy Inference System and clustering method for
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Our approach is also compared with three evolving T2FLSs namely, self evolving interval type-2 fuzzy neural network (SEIT2FNN) utilising IT2FS in the antecedents and TSK interval
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Basic flexible OR-type neuro-fuzzy inference system and basic compromise AND-type neuro-fuzzy inference system are two new flexible neuro-fuzzy controllers
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Our approach is also compared with three evolving T2FLSs namely, self evolving interval type-2 fuzzy neural network (SEIT2FNN) utilising IT2FS in the antecedents and TSK interval
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This work shows the potentiality, simplicity and viability of a fuzzy inference system using only interval type-2 fuzzy sets to instrument fault detection and diagnosis.. The
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A computationally fast interval type-2 neuro-fuzzy inference system and its meta-cognitive projection based learning algorithm, in: 2014 International Joint Conference on
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In this study, the use of decoupled extended Kalman filter (DEKF) to optimize the parameters of an interval type-2 intuitionistic fuzzy logic system of Tagagi-Sugeno-Kang
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ABSTRACT: In this paper, adaptive network based fuzzy inference system (ANFIS) was used in control applications of a Heat Exchanger as interval type-2 fuzzy logic
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The rules used in this model are similar to those used in type-1 model except that the input values in each rule are fuzzified using interval type-2 fuzzy sets.. The output functions
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ABSTRACT: In this paper, adaptive network based fuzzy inference system (ANFIS) was used in control applications of a Shell and Tube Heat Exchanger as interval type-2 fuzzy
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The algorithm uses historic dataset of electric load to generate FOUs and FLRs (Fuzzy Logic Relationship) which along with present and previous hour load values gives multiple
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