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Simulations of Various Applications of Fuzzy Logic using the MATLAB

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Dilpreet Kaur Grover
10.5120/ijca2016909746

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

@article{10.5120/ijca2016909746,
	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 = {http://www.ijcaonline.org/archives/volume141/number13/24846-2016909746},
	doi = {10.5120/ijca2016909746},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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.

References

  1. Fuzzy Logic Toolbox, for use with MATLAB, “The MathWorks”, user’s guide, version 2.
  2. Kiran Pal,Surendra Tyagi, “Selection of Candidate by Political Parties Using Fuzzy Logic”, International Conference of Advance Research and Innovation (ICARI - 2004).
  3. Pedro Albertos and Antonio Sala, “Fuzzy Logic Controllers. Advantages and Disadvantages”, September 14, 1998.
  4. Fuzzy logic, https://en.m.wikipedia.org/wiki/Fuzzy_logic
  5. Fuzzy set operations, https://en.m.wikipedia.org/wiki/Fuzzy_set_operations
  6. Anita Pati, V.K. Singh, K.C. Mishra, “Filtering Noise on two dimensional image Using Fuzzy Logic Technique, International Journal of Scientific & Engineering Research, Volume 2, Issue 3, March-2011, ISSN 2229-5518.
  7. Siddharth Saxena and Rajeev Kumar Singh, “A Novel Approach of Image Restoration on Segmentation and Fuzzy Clustering”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.7, No.4 (2014),pp.255-264.
  8. Nivedita Chakraborty and Minakshi Deb Barma, department of Electrical Engineering, NIT Agartala, Tripura, India, “Modelling of Stand –Alone Wind Energy Conversion System using Fuzzy Logic Controller”, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. 2, Issue 1, January 2014, ISSN (Online) 2321 – 2004, ISSN (Print) 2321 – 5526.
  9. Fuzzy logic, http://reference.wolfram.com/applications/fuzzylogic/Manual/3.html.
  10. Fuzzy set operations, http://www.maplesoft.com/applications/view.aspx?SID=14171&view=html.
  11. Inma P.Cabrera, Pablo Cordero, and Manuel Ojeda- Aciego, “Fuzzy Logic, Soft Computing, and Applications”.
  12. Disha, Mr. Pawan Kumar Pandey, Rajeev Chugh, “Simulation of Water Level Conrol in a Tank Using Fuzzy Logic, IOSR Journal of Electrical and Electronics Engineering (IOSRJEEE), ISSN: 2278-1676 Volume 2, Issue 3 (Sep-Oct. 2012), PP 09-12.
  13. Harshdeep Singh (109ME0422), “Design of Water Level Controller Using Fuzzy Logic System”, National Institute of Technology Rourkela.
  14. Bourdillon O. Omijeh, M. Ehikhamenle, Elechi Promise, “ Simulated Design of Water Level Control System”, Computer Engineering and Intelligent Systems, Vol 6, No.1, 2015.
  15. Tarun Mahashwari, Amit Asthana, “Image Enhancement Using Fuzzy Technique” International Journal of Research Review in Engineering Science & Technology, Volume-2, Issue-2, June-2013.
  16. Er. Mandeep Singh Sandhu, Er. Vikram Matneja, Er. Nishi, “Edge Detection by Using Rule Based Fuzzy Clasifier”, International Journal of Computer Science and Information Technologies, Vol 2 (5), 2011, 2434-2439.
  17. Abdallah A. Alshennawy, and Ayman A. Aly, “Edge Detection in Digital Images Using Fuzzy Logic Technique”, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol:3, No:3, 2009.
  18. Reena Rani, Dushyant Kumar (Asst. Prof), Narinder Singh, “ Design of Adaptive Noise Canceller Using RLS Filter a Review”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 11, November 2012.
  19. MARVIN R. SAMBUR, member, IEEE, “Adaptive Noise Canceling for Speech Signals”, IEEE TRANSACTIONS ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL, ASSP-26, NO.5, OCTOBER 1978.
  20. Sayed. A. Hadei, Student Member IEEE and M. Iotfizad, “A Family of Adaptive Filter Algorithms in Noise Cancellation for Speech Enhancement”, International Journal of Computer and Electrical Engineering, Vol. 2, No. 2, April 2010, 1793-8163.

Keywords

FIS, fuzzy logic, fuzzy rules, membership function.