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A New Algorithm to Model Highly Nonlinear System based Coactive Neuro Fuzzy Inference System

by Tharwat O. S. Hanafy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 17
Year of Publication: 2014
Authors: Tharwat O. S. Hanafy
10.5120/16450-6066

Tharwat O. S. Hanafy . A New Algorithm to Model Highly Nonlinear System based Coactive Neuro Fuzzy Inference System. International Journal of Computer Applications. 94, 17 ( May 2014), 9-20. DOI=10.5120/16450-6066

@article{ 10.5120/16450-6066,
author = { Tharwat O. S. Hanafy },
title = { A New Algorithm to Model Highly Nonlinear System based Coactive Neuro Fuzzy Inference System },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 17 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 9-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number17/16450-6066/ },
doi = { 10.5120/16450-6066 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:17:54.946879+05:30
%A Tharwat O. S. Hanafy
%T A New Algorithm to Model Highly Nonlinear System based Coactive Neuro Fuzzy Inference System
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 17
%P 9-20
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the Fusion of neural and fuzzy Systems will be investigated. Membership Function Generation and its mapping to Neural Network are introduced. . System modeling based on conventional mathematical tools (differential equations) is not well suited for dealing with ill-defined and uncertain systems. By contrast, a fuzzy inference system employing fuzzy if- then rules can model the qualitative aspects of human knowledge and reasoning process without employing precise quantitative analyses. An adaptive network fuzzy inference system (ANFIS) is introduced, and Multiple Inputs /Outputs Systems (Extended ANFIS Algorithm) is implemented. A Modification algorithm of ANFIS, Coupling of ANFIS called coactive neuro fuzzy system (CANFIS), is introduced and implemented. The software of the modified algorithm of MIMO model identification is built and generated by me or added as a toolbox to matlab. To test the validity of the modified algorithm ANFIS (CANFIS algorithm), a coupled inputs-outputs example is simulated from the numerical equation. The result of modified algorithm (CANFIS) showed a conformance with the simulated example and the root mean square (RMSE) is very small.

References
  1. Tharwat O. S. Hanafy, Al-Osaimy, Mosleh M. Al-Harthi "Identification of Uncertain Nonlinear MIMO Spacecraft Systems Using Coactive Neuro Fuzzy Inference System (CANFIS)", International Journal of Control, Automation and Systems,PP. 25-37, Pub. 2014.
  2. Tharwat O. S. Hanafy, Al-Osaimy "Facilitation Rule Base for Solidification of Nonlinear Real Plant System ", International Journal of Control, Automation and Systems, ISSN 2165 – 8277, PP. 1-9, Pub. Date: 2014-01-15.
  3. Tharwat O. S. Hanafy, and Kamel A. Shoush. "Dynamic Evolving Neuro Fuzzy Systems of Qualitative Process " International Journal of Control, Automation and Systems, ISSN 2165 – 8277, PP. 1-9, Pub. Date: 2014-01-15.
  4. Tharwat O. S. Hanafy, H. Zaini, Kamel A. Shoush "Recent Trends in Soft Computing Techniques for Solving Real Time Engineering Problems" International Journal of Control, Automation and Systems, ISSN 2165 – 8277 PP. 27-33, Pub. Date: 2014.
  5. Tharwat O. S. Hanafy, and M. Kamel "Simplification of Rules Base for Inverted Pendulum using ANFIS" Indianan Journals Internationals, 2014
  6. Tharwat. O. Hanafy, "Adaptive Neuro Fuzzy Systems for Dynamic Qualitative Modeling Process" Nature and Sicence Journal ,V 8 , 2010
  7. Tharwat. O. Hanafy, "Design and Validation of Real Time Neuro Fuzzy Controller for Stablization of Pendulum Cart System" Life Sicence Journal ,V 8 ,I 1, 2011.
  8. Tharwat. O. Hanafy, "Neuro Fuzzy Modeling Scheme for the Prediction of Air Polution" Journal of American Science,V 6, 12, 2010.
  9. Tharwat. O. Hanafy, "A modified Algorithm to Model Highly Nonlinear systems " Journal of American Science,V 6, 12, 2010.
  10. Tharwat. O. Hanafy, Tarek Soph, Awd K. "A systematic Algorithm to construct Neuro Fuzzy Inference Systems" 16 th International Conference in software Engineering and Data Engineering July 9-11-2007, ISCA.
  11. Tharwat. O. Hanafy, "Stablization of Inverted Pendulum System using Particular Swarm Optimization" the 8 th International Conference on Informatics and systems INFOS 2012, 14-16 may.
  12. Tharwat. O. Hanafy, Emad Massamir, Osama amr, "Extracting Variables Size Secret Keys From Voice Key", New York Sicence Journal, V 4, 2011.
  13. Tharwat. O. Hanafy, Tarek Soph, Awd K. "Hybrid Learning Rules for Identifying Spacecraft system" 20 th International Conference on Computers and Their Applications, CATA 2005, Orleans, USA.
  14. Tharwat. O. Hanafy, Tarek Soph, Awd K. "Neuro Fuzzy Techniques using Matlab /Simulink Applied to Real Process" 14 th International Conference on Intelligence and Adaptive systems,and soft ware Engineering, IASSE, 2005.
  15. Tharwat. O. Hanafy," Recent Trends in Evolutionary Computations on Organic Mechanism Simulation for Control Systems", Global Advanced Research, technology and Innovation 2012
  16. Hossein Salehfar, Nagy B. , Jun Huang, "Systematic Approach to Linguistic Fuzzy Modeling Based on Input-Output Data ", proceeding of the 2000 Conference.
  17. Jang J. -S. R. "ANFIS: Adaptive Network-based Fuzzy Inference System", IEEE Transactions on Systems, Man and Cybernetics, (1993), 23 (3), 665-685.
  18. E. Kim, M. Park, S. Ji, and M. Park, "A new approach to fuzzy modeling", IEEE Trans. on Fuzzy Syst. , vol. 5, no. 3, pp. 328-337, 1997.
  19. Amine Tabelsi, Frederic, "Identification of Nonlinear Multivariable Systems by Adaptive Fuzzy Takagi-Sugeno Model", International Journal of Computational Cognition,September 2004
  20. Azeem M. F. , Hanmandlu M. and Ahmad N. "Generalization of adaptive neuro-fuzzy inference system", IEEE Transactions on Neural Networks, 11(6), 1332-1346. 2000
  21. Kim J. and Kabasov N. "Adaptive Neuro-Fuzzy inference systems and their application to nonlinear dynamical systems", IEEE Transactions on Neural Networks, (1999), 12 (9), 1301-1319.
  22. Lin C. -T. , "A neural fuzzy control scheme with structure and parameter learning", Fuzzy Sets and Systems, (1995), 70, 183-212.
  23. M. Sugeno and T. Yasukawa, "A fuzzy-logic-based approach to qualitative modeling", IEEE Trans. on Fuzzy Systems, vol. 1, no. 1, pp. 7-31, 1993
  24. Mackey M. C. and Glass L. "Oscillation and chaos in physiological control systems", Science, 1997, 287-289.
  25. Mamdani, E. H. "applications of fuzzy algorithms for control simple dynamic plants, Proc. of the IEE, (1984), 121, 1585-1588.
  26. Nauck D. and Kruse R. , "Neuro-fuzzy systems for function approximation", Fuzzy Sets and Systems, 1999, 101, 261-271.
  27. Pal K. and N. R. Pal. "A neuro-fuzzy system for infringing", International Journal of Intelligent and Fuzzy Systems, 1999, 14(11), 1155-1182.
  28. Ann L Casebeer and Marja J Verhoef, " Combining Qualitative and Quantitative Research Methods: Considering the Possibilities for Enhancing the Study of Chronic Diseases", Volume 18, No. 3 -1997
  29. Paiva R. P. , "Neuro-Fuzzy Identification: Interpretability Issues", MSc Thesis, Department of Informatics Engineering, Faculty of Sciences and Technology, University of Coimbra, Portugal, 1999.
  30. Paiva R. P. , Dourado A. and Duarte B. "Applying subtractive clustering for neuro-fuzzy modeling of a bleaching plant", Proceedings of the European Control Conference -1999, ECC'99, CD-ROM.
  31. Jyh-Shing, Roger Jang "Neuro Fuzzy Modeling and Control", proceeding of the IEEE, March 1995.
  32. R. Fuller, "Introduction to Neuro-Fuzzy Systems, Advances in Soft Computing Series", Springer- Verlag, Berlin, 1999.
  33. Chiu S. L. "Fuzzy model identification based on cluster estimation", Journal of Intelligent and Fuzzy Systems, 1994, 2 (3), 267-278
  34. Dave R. N. and Krish napuram R. "Robust clustering methods: a unified view", IEEE Transactions on Fuzzy Systems, 1997, Vol. 5 No. 2, 270-293.
  35. Robert Shorten "On The Interpretation And Identification of Dynamic Takgi-Sugeno Fuzzy Models". IEEE Trans. on Fuzzy Systems, Vol. 8, No 3, Jun 2000.
  36. R. Babuska J. A. Roubos H. B. Verbruggen, "Identification of MIMO systems by input -output TS fuzzy models" Delft University of Technology, Department of Electrical Engineering, Control Laboratory-Mekelweg, 2004, P. O. Box 5031, 2600 GA Delft, The Netherlands.
  37. Carmona, P. Castro, J. L. Zurita, J. M, "FRIWE: Fuzzy Rule Identification with Exceptions", IEEE Transactions on Fuzzy Systems, Publication Date: Feb. 2004 on page(s):140-151Volume: 12.
  38. Yu, W. Li, X. , "Fuzzy Identification Using Fuzzy Neural Networks With Stable Learning Algorithms", IEEE Transactions on Fuzzy Systems, June 2004 On page(s): 411-420 Volume: 12, Issue:3, ISSN: 1063-6706.
  39. Jang R, Sun C T and Mizutani E. , "Neuro-fuzzy and soft computing: A computational Approach to Learning and Machine Intelligence", Prentice Hall NJ, 1997.
  40. Robert Babuska, "Neuro fuzzy methods for modeling and identification", Delft university of technology, faculty of information technology and systems, Recent advances in intelligent Paradigms and applications, Springer- 2002
  41. Hung T. R. Prasad, Carol L. "First Course in Fuzzy and Neural Control", @2003 by Chapman & Hall/CRC.
  42. Ho Jae Lee, Hagbae Kim, Young Hoon Joo, Wook Chang, Jin Bae Park, "A new intelligent digital redesign for T-S fuzzy systems: global approach" IEEE Transactions on Fuzzy Systems" April 2004 ,Volume:12,Issue:2ISSN: 1063-6706
  43. Jyh-Shung, Roger Jang, 1995, "Neuro Fuzzy Modeling and Control", proceeding of the IEEE, March 1995
Index Terms

Computer Science
Information Sciences

Keywords

Nonlinear System Fuzzy Inference System ANFIS Neural Network