A New Fuzzy Inference Technique for Singleton Type-2 Fuzzy Logic Systems | Zendy

Hwan-Joo Kwak | Zendy; Dong-Won Kim | Zendy; Gwi-Tae Park | Zendy

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A New Fuzzy Inference Technique for Singleton Type-2 Fuzzy Logic Systems

Author(s) -

Hwan-Joo Kwak,

Dong-Won Kim,

Gwi-Tae Park

Publication year - 2012

Publication title -

international journal of advanced robotic systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.394

H-Index - 46

eISSN - 1729-8814

pISSN - 1729-8806

DOI - 10.5772/51187

Subject(s) - adaptive neuro fuzzy inference system , computer science , defuzzification , fuzzy set operations , fuzzy logic , fuzzy number , fuzzy classification , neuro fuzzy , fuzzy electronics , fuzzy control system , singleton , artificial intelligence , fuzzy control language , inference , fuzzy associative matrix , fuzzy set , pregnancy , biology , genetics

A new fuzzy inference technique is presented to replace the conventional fuzzy inference process of type-2 fuzzy logic systems. Because conventional type-2 fuzzy logic systems demand a large amount of memory, they cannot be used by most embedded systems, which do not have enough memory space. To overcome this problem, a new fuzzy inference technique for singleton type-2 fuzzy logic systems is presented in this paper which designs mapping functions from input variables to firing sets and brings out the firing sets directly without using as much memory

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