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Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

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Study and Analysis of Fuzzy Logic

Published on May 2012 by Ashish Verma, Praveen, Deepak Kumar, Shubham Gandhi
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National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011
Foundation of Computer Science USA
RTMC - Number 12
May 2012
Authors: Ashish Verma, Praveen, Deepak Kumar, Shubham Gandhi
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Ashish Verma, Praveen, Deepak Kumar, Shubham Gandhi . Study and Analysis of Fuzzy Logic. National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011. RTMC, 12 (May 2012), 1-5.

@article{
author = { Ashish Verma, Praveen, Deepak Kumar, Shubham Gandhi },
title = { Study and Analysis of Fuzzy Logic },
journal = { National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011 },
issue_date = { May 2012 },
volume = { RTMC },
number = { 12 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 1-5 },
numpages = 5,
url = { /proceedings/rtmc/number12/6706-1104/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011
%A Ashish Verma
%A Praveen
%A Deepak Kumar
%A Shubham Gandhi
%T Study and Analysis of Fuzzy Logic
%J National Workshop-Cum-Conference on Recent Trends in Mathematics and Computing 2011
%@ 0975-8887
%V RTMC
%N 12
%P 1-5
%D 2012
%I International Journal of Computer Applications
Abstract

Fuzzy logic is an outcome of merging the techniques of traditional rule based expert system, set theory and control theory, which is essentially based on mathematical models of the controlled process. Concept of this paper is to demonstrate how fuzzy logic is used as a problem-solving control system methodology that lends itself to implementation in systems ranging from simple, small, embedded micro-controllers to large, networked, multi-channel PC or workstation-based data acquisition and control systems. It can be implemented in hardware, software, or a combination of both. FL provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information. FL's approach to control problems mimics how a person would make decisions, only much faster.

References
  1. Novák, V. , Perfilieva, I. and Mo?ko?, J. (1999) Mathematical principles of fuzzy logic Dodrecht: Kluwer Academic.
  2. L. A. Zadeh, Fuzzy Sets, Information and Control, 1965
  3. L. A. Zadeh, Outline of A New Approach to the Analysis of of Complex Systems and Decision Processes, 1973
  4. L. A. Zadeh, "Fuzzy algorithms," Info. & Ctl. , Vol. 12, 1968, pp. 94-102.
  5. "U. S. Loses Focus on Fuzzy Logic" (Machine Design, June 21, 1990).
  6. "Why the Japanese are Going in for this 'Fuzzy Logic'" by Emily T. Smith (Business Week, Feb. 20, 1993, pp. 39).
  7. "Fuzzy Logic Makes Guesswork of Computer Control" by Gail M. Robinson (Design News,Vol. 47, Nov. 28, 1991, pp. 21).
  8. "Fuzzy Logic Outperforms PID Controller" by P. Basehore (PCIM, March 1993).
  9. Novák, V. Are fuzzy sets a reasonable tool for modeling vague phenomena?, Fuzzy Sets and Systems 156 (2005) 341—348.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Logic