Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems

Alireza Rezaee

doi:doi:10.11648/j.ijmfs.20160204.11

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Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems

Alireza Rezaee

Received: 10 September 2016 Accepted: 29 October 2016 Published: 27 December 2016

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Abstract

This paper proposes a systematic fuzzy model (SFM) to control of general system. SFM model is parted to multiple parts such as: a single parameter in formulation of reasoning; a linear relationship between input and output as a result; the use of evolutionary programming for the selection of the appropriate system parameters and a fuzzy clustering algorithm. Unlike traditional methods of inference mechanism to select a priori reasoning mechanism; SFM model can adjust its parameters using evolutionary programming. To vary the degrees of linear functions of the fuzzy rules, a set of equations describes the system’s input and output locally. Thus, this model can take advantage of the properties of linear systems. Fuzzy rules, the fuzzy c- means clustering algorithm and proper selection of the cluster centers by using evolutionary algorithm have been investigated. Finally, this system has been tested and validated on both controlled robot arm joint.

DOI	10.11648/j.ijmfs.20160204.11
Published in	International Journal of Management and Fuzzy Systems (Volume 2, Issue 4, August 2016)
Page(s)	31-37
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Robot, Arm, Fuzzy Clustering, Evolutionary Programming

References

[1]	W. Pedrycz, fuzzy Sets Engineering. Boca Raton, FL: CRC, 1995.
[2]	B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs, NJ: Prentice-Hall, 1992.
[3]	J. H. Nie and T. H. Lee, “Rule-based modeling: Fast construction and optimal manipulation,” IEEE Transactions on Systems, Man and Cybernetics, A, vol. 26, pp. 728-738, Nov. 1996.
[4]	R. Babuska and H. B. Verbruggen, “Fuzzy set methods for local modeling and identification,” in Multiple Model Approaches to Nonlinear Modeling and Control, R. Murray-Smith and T. A. Johansen, Eds. London, U.K.: Taylor& Francis, 1997, pp. 75-100.
[5]	M. Sugeno and T. Yasukawa, “A fuzzy-logic-based approach to qualitative modeling,” IEEE Transactions on Fuzzy Systems, 1, No. 1:7-31, 1993.
[6]	M. R. Emami, I. B. Turksen, A. A. Goldenberg, “An improved fuzzy modeling algorithm, part II: System identification,” NAFIPS, 1996.
[7]	K. Al-Sultan. A Tabu Search Approach to Clustering problems pattern recognition, Vol. 28, pp.1443-1451, 1995.
[8]	S. Chiu, “Method and software for extracting fuzzy classification rules by subtractive clustering,” NAFIPS, 1996.
[9]	A. Rezaee, “PSO for fuzzy goal programming,” Appl. Comput. Math, 5(2):218-226, 2006.
[10]	M. Setnes, R. Babuska, and H. B. Verbruggen. “Rule-based modeling: Precision and transparency.” IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 28(1): 165-169, 1998.
[11]	R. R. Yager and D. P. Filev, Essentials of Fuzzy Modeling and Control, John Wiley & Sons, Inc, 1994.
[12]	D. Dubois, H. Prade, Fuzzy Sets and Systems: Theory and Applications. Academic Press: New York, 1980.
[13]	X. Yao, “Evolving artificial neural networks,” Proceedings of the IEEE, 87(9): 1423-1447, September 1999.
[14]	J.-S. R. Jang and N. Gulley, “The Fuzzy Logic Toolbox for Use with MATLAB,” The MathWorks, Inc., Natick, Massachusetts, 1995.
[15]	www.mathworks.com

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Alireza Rezaee. (2016). Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems. International Journal of Management and Fuzzy Systems, 2(4), 31-37. https://doi.org/10.11648/j.ijmfs.20160204.11

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ACS Style

Alireza Rezaee. Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems. Int. J. Manag. Fuzzy Syst. 2016, 2(4), 31-37. doi: 10.11648/j.ijmfs.20160204.11

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AMA Style

Alireza Rezaee. Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems. Int J Manag Fuzzy Syst. 2016;2(4):31-37. doi: 10.11648/j.ijmfs.20160204.11

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@article{10.11648/j.ijmfs.20160204.11,
  author = {Alireza Rezaee},
  title = {Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems},
  journal = {International Journal of Management and Fuzzy Systems},
  volume = {2},
  number = {4},
  pages = {31-37},
  doi = {10.11648/j.ijmfs.20160204.11},
  url = {https://doi.org/10.11648/j.ijmfs.20160204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmfs.20160204.11},
  abstract = {This paper proposes a systematic fuzzy model (SFM) to control of general system. SFM model is parted to multiple parts such as: a single parameter in formulation of reasoning; a linear relationship between input and output as a result; the use of evolutionary programming for the selection of the appropriate system parameters and a fuzzy clustering algorithm. Unlike traditional methods of inference mechanism to select a priori reasoning mechanism; SFM model can adjust its parameters using evolutionary programming. To vary the degrees of linear functions of the fuzzy rules, a set of equations describes the system’s input and output locally. Thus, this model can take advantage of the properties of linear systems. Fuzzy rules, the fuzzy c- means clustering algorithm and proper selection of the cluster centers by using evolutionary algorithm have been investigated. Finally, this system has been tested and validated on both controlled robot arm joint.},
 year = {2016}
}

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TY  - JOUR
T1  - Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems
AU  - Alireza Rezaee
Y1  - 2016/12/27
PY  - 2016
N1  - https://doi.org/10.11648/j.ijmfs.20160204.11
DO  - 10.11648/j.ijmfs.20160204.11
T2  - International Journal of Management and Fuzzy Systems
JF  - International Journal of Management and Fuzzy Systems
JO  - International Journal of Management and Fuzzy Systems
SP  - 31
EP  - 37
PB  - Science Publishing Group
SN  - 2575-4947
UR  - https://doi.org/10.11648/j.ijmfs.20160204.11
AB  - This paper proposes a systematic fuzzy model (SFM) to control of general system. SFM model is parted to multiple parts such as: a single parameter in formulation of reasoning; a linear relationship between input and output as a result; the use of evolutionary programming for the selection of the appropriate system parameters and a fuzzy clustering algorithm. Unlike traditional methods of inference mechanism to select a priori reasoning mechanism; SFM model can adjust its parameters using evolutionary programming. To vary the degrees of linear functions of the fuzzy rules, a set of equations describes the system’s input and output locally. Thus, this model can take advantage of the properties of linear systems. Fuzzy rules, the fuzzy c- means clustering algorithm and proper selection of the cluster centers by using evolutionary algorithm have been investigated. Finally, this system has been tested and validated on both controlled robot arm joint.
VL  - 2
IS  - 4
ER  -

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Author Information

Alireza Rezaee

Department of System and Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran

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APA Style

Alireza Rezaee. (2016). Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems. International Journal of Management and Fuzzy Systems, 2(4), 31-37. https://doi.org/10.11648/j.ijmfs.20160204.11

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ACS Style

Alireza Rezaee. Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems. Int. J. Manag. Fuzzy Syst. 2016, 2(4), 31-37. doi: 10.11648/j.ijmfs.20160204.11

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AMA Style

Alireza Rezaee. Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems. Int J Manag Fuzzy Syst. 2016;2(4):31-37. doi: 10.11648/j.ijmfs.20160204.11

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@article{10.11648/j.ijmfs.20160204.11,
  author = {Alireza Rezaee},
  title = {Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems},
  journal = {International Journal of Management and Fuzzy Systems},
  volume = {2},
  number = {4},
  pages = {31-37},
  doi = {10.11648/j.ijmfs.20160204.11},
  url = {https://doi.org/10.11648/j.ijmfs.20160204.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmfs.20160204.11},
  abstract = {This paper proposes a systematic fuzzy model (SFM) to control of general system. SFM model is parted to multiple parts such as: a single parameter in formulation of reasoning; a linear relationship between input and output as a result; the use of evolutionary programming for the selection of the appropriate system parameters and a fuzzy clustering algorithm. Unlike traditional methods of inference mechanism to select a priori reasoning mechanism; SFM model can adjust its parameters using evolutionary programming. To vary the degrees of linear functions of the fuzzy rules, a set of equations describes the system’s input and output locally. Thus, this model can take advantage of the properties of linear systems. Fuzzy rules, the fuzzy c- means clustering algorithm and proper selection of the cluster centers by using evolutionary algorithm have been investigated. Finally, this system has been tested and validated on both controlled robot arm joint.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Tuning of Systematic Fuzzy Model by Using Evolutionary Algorithm for Control of General Systems
AU  - Alireza Rezaee
Y1  - 2016/12/27
PY  - 2016
N1  - https://doi.org/10.11648/j.ijmfs.20160204.11
DO  - 10.11648/j.ijmfs.20160204.11
T2  - International Journal of Management and Fuzzy Systems
JF  - International Journal of Management and Fuzzy Systems
JO  - International Journal of Management and Fuzzy Systems
SP  - 31
EP  - 37
PB  - Science Publishing Group
SN  - 2575-4947
UR  - https://doi.org/10.11648/j.ijmfs.20160204.11
AB  - This paper proposes a systematic fuzzy model (SFM) to control of general system. SFM model is parted to multiple parts such as: a single parameter in formulation of reasoning; a linear relationship between input and output as a result; the use of evolutionary programming for the selection of the appropriate system parameters and a fuzzy clustering algorithm. Unlike traditional methods of inference mechanism to select a priori reasoning mechanism; SFM model can adjust its parameters using evolutionary programming. To vary the degrees of linear functions of the fuzzy rules, a set of equations describes the system’s input and output locally. Thus, this model can take advantage of the properties of linear systems. Fuzzy rules, the fuzzy c- means clustering algorithm and proper selection of the cluster centers by using evolutionary algorithm have been investigated. Finally, this system has been tested and validated on both controlled robot arm joint.
VL  - 2
IS  - 4
ER  -

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