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A Review of Neuro-Fuzzy Systems Based on Intelligent Control

Received: 5 January 2015     Accepted: 8 January 2015     Published: 23 January 2015
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Abstract

The system's ability to adapt and self-organize are two key factors when it comes to how well the system can survive the changes to the environment and the plant they work within. Intelligent control improves these two factors in controllers. Considering the increasing complexity of dynamic systems along with their need for feedback controls, using more complicated controls has become necessary and intelligent control can be a suitable response to this necessity. This paper briefly describes the structure of intelligent control and provides a review on fuzzy logic and neural networks which are some of the base methods for intelligent control. The different aspects of these two methods are then compared together and an example of a combined method is presented.

Published in Journal of Electrical and Electronic Engineering (Volume 3, Issue 2-1)

This article belongs to the Special Issue Research and Practices in Electrical and Electronic Engineering in Developing Countries

DOI 10.11648/j.jeee.s.2015030201.23
Page(s) 58-61
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), 2015. Published by Science Publishing Group

Keywords

Intelligent control, Neural networks, Fuzzy logic, Neuro-fuzzy

References
[1] C. T. Lin, and C.S.G. Lee. “Neural network based fuzzy logic control and decision system,” IEEE. Trans. Comput, vol. 40, pp. 1320-1336, December 1991.
[2] A. Abraham. “Neuro fuzzy systems: state-of-the-art modeling techniques, connectionist models of neurons,” LNCS, vol. 2084, pp. 269-276, Jun 2001
[3] C. J. Harris, C. G. Moore, and M. Brown. “Intelligent control: aspects of fuzzy logic and neural network,” World Scientific Series in Robotics and Automated Systems. UK, vol. 6, 1993.
[4] B. Bavarian. “Introduction to neural networks for intelligent control,ˮ IEEE Control System Magazine, California, vol. 8, pp. 3-7, April 1988.
[5] C. C. Lee. “Fuzzy logic in control systems: fuzzy logic controller- part I,ˮ IEEE. Trans. Syst. Man. Cyber. Syst. California, vol. 20, pp. 404-418, Mar/Apr 1990.
Cite This Article
  • APA Style

    Fatemeh Zahedi, Zahra Zahedi. (2015). A Review of Neuro-Fuzzy Systems Based on Intelligent Control. Journal of Electrical and Electronic Engineering, 3(2-1), 58-61. https://doi.org/10.11648/j.jeee.s.2015030201.23

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    ACS Style

    Fatemeh Zahedi; Zahra Zahedi. A Review of Neuro-Fuzzy Systems Based on Intelligent Control. J. Electr. Electron. Eng. 2015, 3(2-1), 58-61. doi: 10.11648/j.jeee.s.2015030201.23

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    AMA Style

    Fatemeh Zahedi, Zahra Zahedi. A Review of Neuro-Fuzzy Systems Based on Intelligent Control. J Electr Electron Eng. 2015;3(2-1):58-61. doi: 10.11648/j.jeee.s.2015030201.23

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  • @article{10.11648/j.jeee.s.2015030201.23,
      author = {Fatemeh Zahedi and Zahra Zahedi},
      title = {A Review of Neuro-Fuzzy Systems Based on Intelligent Control},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {3},
      number = {2-1},
      pages = {58-61},
      doi = {10.11648/j.jeee.s.2015030201.23},
      url = {https://doi.org/10.11648/j.jeee.s.2015030201.23},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.s.2015030201.23},
      abstract = {The system's ability to adapt and self-organize are two key factors when it comes to how well the system can survive the changes to the environment and the plant they work within. Intelligent control improves these two factors in controllers. Considering the increasing complexity of dynamic systems along with their need for feedback controls, using more complicated controls has become necessary and intelligent control can be a suitable response to this necessity. This paper briefly describes the structure of intelligent control and provides a review on fuzzy logic and neural networks which are some of the base methods for intelligent control. The different aspects of these two methods are then compared together and an example of a combined method is presented.},
     year = {2015}
    }
    

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    AB  - The system's ability to adapt and self-organize are two key factors when it comes to how well the system can survive the changes to the environment and the plant they work within. Intelligent control improves these two factors in controllers. Considering the increasing complexity of dynamic systems along with their need for feedback controls, using more complicated controls has become necessary and intelligent control can be a suitable response to this necessity. This paper briefly describes the structure of intelligent control and provides a review on fuzzy logic and neural networks which are some of the base methods for intelligent control. The different aspects of these two methods are then compared together and an example of a combined method is presented.
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Author Information
  • Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

  • Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

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