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A Genetic based Neuro-Fuzzy Controller System

by Mohamed, A. H
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 1
Year of Publication: 2014
Authors: Mohamed, A. H
10.5120/16306-5532

Mohamed, A. H . A Genetic based Neuro-Fuzzy Controller System. International Journal of Computer Applications. 94, 1 ( May 2014), 14-17. DOI=10.5120/16306-5532

@article{ 10.5120/16306-5532,
author = { Mohamed, A. H },
title = { A Genetic based Neuro-Fuzzy Controller System },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 1 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 14-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number1/16306-5532/ },
doi = { 10.5120/16306-5532 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:16:25.474528+05:30
%A Mohamed
%A A. H
%T A Genetic based Neuro-Fuzzy Controller System
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 1
%P 14-17
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recently, the mobile robots have great importance in the manufacturing processes. They are widely used for assembling processes, handling the dangerous components, moving the weighted things, etc. Designing the controller of the mobile robot is a very complex task. Many simple control systems used the neuro-fuzzy controller in the mobile robots. But, they faced with great complexity when moving in unstructured and dynamic environments. The proposed system introduces the uses of the genetic algorithm for optimizing the parameters of the neuro-fuzzy controller. So, the proposed system can improve the performance of the mobile robots. It has applied for a mobile robot used for moving the dangerous and critical materials in unstructured environment. Its results are compared with other traditional controller systems. The suggested system has proved its success for the real-time applications.

References
  1. I. Dumitrache and M. Dragocea, "Some Problems of Advanced Mobile Robot Control, Extraction Of Fuzzy Rules Using A Self-Organising Fuzzy Neural Network", Fuzzy Sets System, (2006), Vol. 150, No. 2, pp. 211-243.
  2. D. Nauck, "Neuro-fuzzy systems: Review and prospects". Proc. 5th Euro. Congr. Intelligent Techniques and Soft Computing (EUFIT'97), (1997), pp. 1044-1053.
  3. A. Keles, A. Keles and U. Yavuz, "Expert System Based on Neuro-Fuzzy Rules for Diagnosis Breast Cancer, Expert Systems with Applications, (2011), Vol. 38, No. 5, pp. 5719-5726.
  4. S. Bouzaida and A. Sakly, "Online Control of Nonlinear Systems using Neuro-Fuzzy Design tuned with Cooperative Particle Sub-Swarms Optimization", Proceedings of International Conference on Control, Engineering & Information Technology (CEIT'13), (2013), Vol. 4, pp. 6-10.
  5. S. Wu, M. J. Er and Y. Gao, "A Fast Approach for Automatic Generation of Fuzzy Rules by Generalized Dynamic Fuzzy Neural Networks". IEEE Trans. Fuzzy System, (2001), Vol. 9, No. 4, pp. 578-584.
  6. R. Stathacopoulou, G. D. Magoulas, M. Grigoriadou and M. Samarakou, (2011), "Neural-Fuzzy Knowledge Processing in Intelligent Learning Interactive Neuro-Fuzzy Expert System for Diagnosis of Leukemiam Global", Journals Inc. pp. 112-130.
  7. S. Kar, S. Das, P. K. Ghosh, (2014), "Applications of Neuro Fuzzy Systems: A Brief Review and Future Outline", Applied Soft Computing, Vol. 15, pp. 243-259.
  8. O. O. Obe and I. Dumitrache, (2012), "Adaptive Neuro-Fuzzy Controler With Genetic Training For Mobile Robot Control", Int. J. of Computers, Communications & Control, Vol. 6, No. 1, pp. 135-146.
  9. A. A. Ammar, K. Vamaraju, P. Mukherjee and J. Gorchynski, (2011), "Optimized Fuzzy Logic Training of Neural Networks for Autonomous Robotics Applications", International Journal of Scientific & Engineering Research. 2(10):201-214.
  10. Jang, J. ; Sun, C. and Mizutami, E. (1997), "Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence". New Jersey: Prentice Hall, First ed. , pp. 123-153.
  11. L. Cristaldi, M. Lazzaroni, A. Monti, and F. Ponci, (2004), "A Genetic Algorithm For Fault Identification in Electrical Drives: A Comparison With Neuro-Fuzzy Computation", Proceedings of the Instrumentation and Measurement Technology Conference (IMTC 04), Vol. 2, pp. 1454-1459.
  12. T. Tettey, and T. Marwala, (2006), "Controlling Interstate Conflict using Neuro-fuzzy Modeling and Genetic Algorithms", INES 2006 – 10th International Conference on Intelligent Engineering Systems, pp. 1210-1218.
  13. G. Leng, T. M. Martin, and G. Prasad, (2006), "Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms", IEEE Transactions on Fuzzy Systems, Vol. 14, No. 6, pp. 755-765.
  14. O. O. Obe, and I. Dumitrache, (2010), "Fuzzy Control of Autonomous Mobile Robot", U. P. B. Sci. Bull. , Series C, Vol. 72, No. 3, pp. 150-162.
  15. G. Leng, G. Prasad and T. M. McGinnity, (2004), "An On-Line Algorithm for Creating Self-Organizing Fuzzy Neural Networks", Neural Networks, Vol. 17, pp. 1477-1483.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy controller Neuro-fuzzy Controller Optimization Modeling Genetic algorithm.

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