Implementing Interval Type-2 Fuzzy Processors [Developmental Tools]

Melgarejo, Miguel; Peña-Reyes, Carlos Andrés

doi:10.1109/mci.2007.357195

Cited by 76 publications

(20 citation statements)

References 4 publications

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“…Although the servo case is relatively slow, all these defuzzification methods can be analyzed and compared. Several studies have been implemented in FPGA hardware for T1FLS [16] and IT2FLS [17]. However, no comparisons have been presented, in hardware terms, where the most important defuzzification methods are applied for solving the same problem.…”

Section: Methodsmentioning

confidence: 99%

Real-Time Fuzzy Logic Controllers

Ponce-Cruz

Molina

MacCleery

2015

Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA

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Nonlinear control involves a nonlinear relationship between the controller's inputs and outputs and is more complicated than linear control; however, it is able to achieve better performance than linear control for many real-world control applications. Nonlinear control theory requires more challenging mathematical analysis and design than does linear control theory. An FLC is a nonlinear controller, that is, the function f(x) is nonlinear. What distinguishes an FLC, T1 or T2, from other nonlinear controllers is that it generates its nonlinear mapping function f(x) through linguistic if-then rules and linguistic terms for the antecedents and consequents of the rules (e.g., Low Speed, High Speed). Such rules can be (easily) obtained from a human operator or can be postulated and learned from data. According to Kosko [1], an FLC is unique in that it ties vague words like Low and High, and common sense rules, to state-space geometry [2]. Fuzzy logic controllers have two important advantages over other classes of nonlinear controllers.

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Section: Methodsmentioning

confidence: 99%

Real-Time Fuzzy Logic Controllers

Ponce-Cruz

Molina

MacCleery

2015

Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA

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“…the uncertainty about different perceptions and concepts. Mendel [15,16,18,21,24,25] and Melgarejo [19,20] provided formal definitions of IT2FS. Figueroa [4,5,6,7] proposed an extension of the FLP which involves linguistic uncertainty using IT2FS called Interval Type-2 Fuzzy Linear Programming (IT2FLP).…”

Section: Interval Type-2 Fuzzy Constraintsmentioning

confidence: 99%

A method for solving linear programming models with Interval Type-2 fuzzy constraints

Figueroa–García

Hernández

2014

Pesqui. Oper.

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ABSTRACT. This paper shows a method for solving linear programming problems that includes Interval Type-2 fuzzy constraints. The proposed method finds an optimal solution in these conditions using convex optimization techniques. Some feasibility conditions are presented, and some interpretation issues are discussed. An introductory example is solved using the proposed method, and its results are described and discussed.

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“…Pipelined architecture technique is also popular for achieving higher inferencing performance. A digital fuzzy processor with pipeline hardware architecture is proposed by Melgarejo and Pena-Reyes [13]. Aranguren et al [9] present a 5-level pipeline sequential fuzzy inference processor architecture.…”

Section: Previous Work On Fuzzy Inference Processormentioning

confidence: 99%