Fuzzy Inference System: Short Review and Design

Naseer Sabri(1*), S. A. Aljunid(2), M. S. Salim(3), R. B. Badlishah(4), R. Kamaruddin(5), M. F. Abd Malek(6)

(1) School of Computer and Communication Engineering, University Malaysia Perlis, Malaysia
(2) School of Computer and Communication Engineering, University Malaysia Perlis, Malaysia
(3) School of Mechatronics Engineering, University Malaysia Perlis., Malaysia
(4) School of Computer and Communication Engineering, University Malaysia Perlis, Malaysia
(5) School of Bioprocess Engineering, University Malaysia Perlis., Malaysia
(6) School of Computer and Communication Engineering, University Malaysia Perlis, Malaysia
(*) Corresponding author


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Abstract


Fuzzy control provides a formal methodology for representing, manipulating, and implementing a human’s heuristic knowledge about how to control a system. The fuzzy control can be applied in cases where the control processes are too complex to analyze by conventional quantitative techniques or the available sources of information are interpreted qualitatively, inexactly, or uncertainly. Fuzzy controller offers many advantages to compare with other approaches to deal with uncertainty. Furthermore, fuzzy logic is well suited for low-cost implementations based on low-priced sensors, small resolution convertors of analog to digital and 4-8bit microcontroller. Such systems can be effortlessly improved by adding new rules to enhance performance or add new features. In several cases, fuzzy control can be used to advance existing conventional control systems by appending an additional layer of intelligence to the existing control method. In this research, an extensive insight of Fuzzy Inference System steps design are presented, as well as, the emphases on  the fusion of fuzzy control concepts with nowadays wireless sensor networks. Review of fuzzy superior features contrary to traditional and intelligent controlling paradigms is obviously stated. Finally, the adoption of Fuzzy Inference System (FIS) approach to control problems with wireless sensor actor network is due to a number of reasons. Its algorithm needs a small size of memory, less time of execution; indeed it necessitates less mathematics than other controlling approaches.
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Keywords


Fuzzy Logic Concepts; Fuzzy Inference System; Fuzzy Features; Fuzzy Controller Types

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