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Simulations of Various Applications of Fuzzy Logic using the MATLAB

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Dilpreet Kaur Grover

Dilpreet Kaur Grover. Simulations of Various Applications of Fuzzy Logic using the MATLAB. International Journal of Computer Applications 141(13):39-45, May 2016. BibTeX

	author = {Dilpreet Kaur Grover},
	title = {Simulations of Various Applications of Fuzzy Logic using the MATLAB},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2016},
	volume = {141},
	number = {13},
	month = {May},
	year = {2016},
	issn = {0975-8887},
	pages = {39-45},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2016909746},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


With the increasing problems of uncertainty, vagueness and imprecision during the modeling of various control system, the fuzzy logic comes into account. Fuzzy logic was first introduced by a Polish philosopher, Jan Lukasiewicz, in 1930. As the classical logic operates with only two values- 1 (true) and 0 (false), Lukasiewicz introduced a new logic which has more than two truth values. Lukasiewicz extends the range of classical logic to all the real numbers in the interval between 0 and 1 and named it as fuzzy logic. Fuzzy logic is a powerful tool which represents and process human knowledge in the form of fuzzy if-then rules. As the fuzzy logic systems is based on human thinking and natural language and also has a good stability, fast response and less complexity, the applications based on fuzzy logic have been increased significantly. To follow the trend, this paper presents the basic introduction of fuzzy logic, fuzzy sets and its operations. This paper provides a huge description of fuzzy logic system and fuzzy inference system and also provides comparison between fuzzy logic system and conventional control system. In this paper, the various applications of fuzzy logic have been simulated using the MATLAB.


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FIS, fuzzy logic, fuzzy rules, membership function.