A high speed scalable and reconfigurable fuzzy controller

Homburg, F.; Palomera-Garcia, R.

doi:10.1109/iscas.2003.1206433

Cited by 6 publications

(9 citation statements)

References 5 publications

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“…In The specification of this proposed MtFIP is listed in Table 6.2. Some high flexibility fuzzy inference processors and high inference performance processors reported in [85,93,117,127,132,135] are also listed in the same table. From a performance viewpoint, the MtFIP aims to incorporate the advantages of both high flexibility and high inference performance.…”

Section: Situationmentioning

confidence: 99%

“…The fuzzy inference processors reported in [117,135] School of EEE Nanyang Technological University Table 6.2: MtFIP specifications and comparisons fall into the high flexibility category, while the fuzzy processors in [85,127,132] fall into the high inference performance category. The MtFIP, RcFIP and the processors in [117,127,132] are implemented using FPGAs. The processor in [85] is implemented with ASIC, while the processor in [135] is implemented with Microcontroller.…”

Section: Situationmentioning

confidence: 99%

“…Nanyang Technological University LibraryThe specification of this RcFIP is listed inTable 4.2. Several digital fuzzy inference processors reported in[65,80,85,120,127,132,133] are also listed in the same table. The RcFIP is the combination of the implicit and explicit implementation approaches.…”

mentioning

confidence: 99%

“…The fuzzy processor in[65] adopts the implicit implementation, while the fuzzy processors in[80,85,120,127,132,133] adopt the explicit implementation. The RcFIP and the processors in[65,120,127,132,133] are implemented using FPGAs, while the processors in[80,85] are implemented with ASICs. RcFIP specifications and comparisons It is observed from Table 4.2 that the proposed RcFIP incorporated the advantages of both implicit and explicit implementations fuzzy inference processors.…”

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confidence: 99%

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Design and implementation of a reconfigurable fuzzy inference processor

Cao¹

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I would like to thank many people who have supported me during my study in Nanyang Technological University. First and foremost, I would like to express my sincere gratitude and appreciation to my supervisor, Assoc. Prof. Lim Meng Hiot, for his professional guidance and support, for his invaluable advice and encouragement throughout my Ph.D. program. I benefit a lot from his supervision over the years. I really appreciate his style of supervision and his patience to his students. I would like to appreciate Assoc. Prof. Ling Keck Voon, for his apparatus support and helpful discussions in my project. I also appreciate Mr. Sia Liang Poo, Mr.

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Section: Situationmentioning

confidence: 99%

Section: Situationmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Design and implementation of a reconfigurable fuzzy inference processor

Cao¹

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“…Given a set of input data, the fuzzy inference processor evaluates the proposed actions in terms of its conformity to the knowledge base. A lot of VLSI hardware implementations [2][3][4][5][6][7] have been proposed to support this typical fuzzy inference execution. However, in fact, there are uncertainties associated with the knowledge base.…”

Section: Introductionmentioning

confidence: 99%

VLSI Implementation of Type-2 Fuzzy Inference Processor

Huang

Chen

2005 IEEE International Symposium on Circuits and Systems

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The main concept of type-2 fuzzy logic is that "words mean different things to different people"; thus, there are uncertainties associated with words. Recently, a lot of mathematical theorems have been developed for type-2 fuzzy logic. However, to the best of knowledge, there has been no hardware implementation. In this paper, we propose the first hardware architecture to support the type-2 fuzzy inference execution. Moreover, we also use a 0.35μm cell library to implement the proposed architecture. Implementation data show that the inference speed reaches up to 3.125 MFLIPS with 2 inputs, 1 output and 64 rules.

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