Neuro Fuzzy Inference Approach : A Survey

Authors(2) :-Tigilu Mitiku, Mukhdeep Singh Manshahia

Fuzzy Logic is an extension of classical logic which provides an effective mathematical tool to represent information in a way that resembles natural human reasoning and deals with system uncertainty and vagueness. ANN is a biologically inspired computational structure comprised of densely interconnected adaptive simple processing elements that are capable of performing massively parallel computations for data processing and knowledge representation. The combination of fuzzy inference system and artificial neural network have attracted the researchers and scholars in various scientific and engineering areas to the growing need of adaptive intelligent systems. Artificial neural network are not good at explaining how they reach their decisions whereas fuzzy systems, which can reason with imprecise information, are good at explaining their decisions but they cannot automatically acquire the rules they use to make those decisions. Due to these limitations an intelligent hybrid systems where two or more techniques are combined in a manner that overcomes the problems of individual techniques are created. Any type of systems that combine these two techniques can be called Neuro-Fuzzy systems. Neuro-Fuzzy systems are systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. This type of system is characterized by a fuzzy system where fuzzy sets and fuzzy rules are adjusted using input output patterns. There are several types of neuro-fuzzy systems where each author defined its own model. This survey paper describes the most known hybrid neuro-fuzzy techniques, with their advantages and Limitations.

Authors and Affiliations

Tigilu Mitiku
Department of Mathematics, Punjabi University Patiala, Punjab, India
Mukhdeep Singh Manshahia
Department of Mathematics, Punjabi University Patiala, Punjab, India

Fuzzy Logic, Neural Networks, Neuro Fuzzy Systems

  1. LA. Zadeh, "Soft computing and fuzzy logic. ," IEEE software, vol. 11, no. 6, pp. 48-56, 1994.
  2. Lotfi A.Zadeh L Zadeh, "Fuzzy sets," Information and Control, vol. 8, no. 3, pp. 338-353, 1995.
  3. Jerry M., and George C. Mouzouris. Mendel, "Designing fuzzy logic systems.," IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing., vol. 44, no. 11, pp. 885-895, 1997.
  4. Dogan. Ibrahim, "An overview of soft computing.," Procedia Computer Science., vol. 102, pp. 34-38, 2016.
  5. Andrew G., and Richard S. Sutton. Barto, "Reinforcement learning. ," Neural systems for control., pp. 7-29, 1998.
  6. Jose, F. Morgado Dias, and Alexandre Mota. Vieira, "Neuro-fuzzy systems: a survey.," In 5th WSEAS NNA International Conference on Neural Networks and Applications, Udine, Italia. , vol. 3, pp. 414-419, March 2004.
  7. JS. Jang, "ANFIS: adaptive-network-based fuzzy inference system. ," IEEE transactions on systems, man, and cybernetics., vol. 23, no. 3, pp. 665-685, 1993.
  8. Chun-Tian, Jian-Yi Lin, Ying-Guang Sun, and Kwokwing Chau. Cheng, "Long-term prediction of discharges in Manwan Hydropower using adaptive-network-based fuzzy inference systems models.," in In International Conference on Natural Computation. , vol. 3612, Springer, Berlin, Heidelberg., 2005, pp. 1152-1161.
  9. Hamid R., and Pratap Khedkar. Berenji, "Learning and tuning fuzzy logic controllers through reinforcements. ," IEEE Transactions on neural networks 3, no. 5 (1992): 724-740., vol. 3, no. 5, pp. 724-740, 1992.
  10. C-T., and C. S. George Lee. Lin, "Neural-network-based fuzzy logic control and decision system.," IEEE Transactions on computers , vol. 40, no. 12, pp. 1320-1336, 1991.
  11. Detlef, and Rudolf Kruse. Nauck, "Neuro-fuzzy systems for function approximation.," Fuzzy sets and systems., vol. 101, no. 2, pp. 261-271, 1999.
  12. Detlauf, and Rudolf Kruse. Nauck, "NEFCLASSmdash; a neuro-fuzzy approach for the classification of data. ," in In Proceedings of the 1995 ACM symposium on applied computing., 1995, pp. 461-465.
  13. Tom M., and Bert Kappen. Heskes, "On-line learning processes in artificial neural networks. ," North-Holland Mathematical Library., vol. 51, pp. 199-233, 1993.
  14. N. Kasabov, "A framework for evolving connectionist systems and the" eco" training method.," in In proceedings of ICONIP, pp. 1232-1235.
  15. Nikola K. Kasabov, "Adaptable neuro production systems. ," Neurocomputing , vol. 13, no. 2, pp. 95-117, 1996.
  16. Michael C., and Paul Smolensky. Mozer, "Skeletonization: A technique for trimming the fat from a network via relevance assessment.," In Advances in neural information processing systems., pp. 107-115, 1989.
  17. Teuvo. Kohonen, "The self-organizing map. ," in proceedings of the ieee, 78., 1990.
  18. Nikola K., and Qun Song. Kasabov, Dynamic Evolving Fuzzy Neural Networks with" m-out-of-n" Activation Nodes for On-line Adaptive Systems. Department of Information Science.: University of Otago, 1999.
  19. Nikola. Kasabov, "Evolving fuzzy neural networks for adaptive, on-line intelligent agents and systems. ," Recent Advances in Mechatronics, pp. 27-41, 1999.
  20. Ajith. Abraham, "Neuro Fuzzy Systems: state of Art Modelling Techniques," in In proceedings of the sixth international work conference on Artificial and Natural Neural Networks., 2001.
  21. Ajith, and Baikunth Nath. Abraham, "Hybrid intelligent systems design: A review of a decade of research. ," IEEE Trans-actions on Systems, Man and Cybernetics (Part C)., vol. 3, no. 1, pp. 1-37, 2000.
  22. Jose, F. Morgado Dias, and Alexandre Mota. Vieira, "Neuro-fuzzy systems: a survey.," In 5th WSEAS NNA International Conference on Neural Networks and Applications, Udine, Italia. , 2004.
  23. Shun'ichi, Takuya Oyama, and Thierry Arnould. Tano, "Deep combination of fuzzy inference and neural network in fuzzy inference software?INEST. ," Fuzzy Sets and Systems., vol. 82, no. 2, pp. 151-160, 1996.
  24. Samarjit, Sujit Das, and Pijush Kanti Ghosh. Kar, "Applications of neuro fuzzy systems: A brief review and future outline," Applied Soft Computing , vol. 15, pp. 243-259, 2014.
  25. Regina, George D. Magoulas, Maria Grigoriadou, and Maria Samarakou. Stathacopoulou, "Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis. ," Information Sciences., vol. 170, no. 2, pp. 273-307, 2005.
  26. Luigi, Gianguido Rizzotto, Mario Lavorgna, Giuseppe Nunnari, M. Gabriella Xibilia, and Riccardo Caponetto. Fortuna, Soft computing: new trends and applications.: Springer Science & Business Media, 2012.
  27. S.Sumathi and S.N.Deepa S.N.Sivananadam, Introduction to Fuzzy Logic using Matlab.: Springer-Verlag Berlin Heidelberg , 2007.
  28. Ph.D., D.Sc.,Professor Jacek ~ski, Ph.D., D.Sc. Professor Ernest Czogalat, Fuzzy and N euro-Fuzzy Intelligent Systems, Prof. Janusz Kacprzyk, Ed. Poland: A Springer-Verlag Company.
  29. C., B. Krause, and H-J. Zimmermann. Von Altrock, "Advanced fuzzy logic control technologies in automotive applications.," In Fuzzy Systems, 1992., IEEE International Conference , pp. 835-842, 1992.
  30. Kazuo, and Hideyuki Takagi. Asakawa, "Neural networks in Japan.," Communications of the ACM , vol. 37, no. 3, pp. 106-112, 1994.
  31. J. S. R. Jang, "Neuro-fuzzy modeling: architectures, analyses and applications [dissertation]. ," California: University of Berkeley), 1992.
  32. J.S. Jang, "ANFIS: adaptive-network-based fuzzy inference system.," IEEE transactions on systems, man and cybernetics., vol. 23, no. 3, pp. 665-685, 1993.
  33. S. R., P. J. Nikumbh, and S. P. Kulkarni. Nikam, "Fuzzy logic and neuro-fuzzy modeling. ," Journal of Artificial Intelligence., vol. 3, no. 2, p. 74, 2012.
  34. Ebrahim H., and Sedrak Assilian. Mamdani, "An experiment in linguistic synthesis with a fuzzy logic controller.," International journal of man-machine studies, vol. 7, no. 1, pp. 1-13, 1975.
  35. Ajith. Abraham, "Adaptation of fuzzy inference system using neural learning.," Fuzzy systems engineering (2005): 914-914., pp. 914-914, 2005.
  36. Lotfi A.Zadeh L Zadeh, "Fuzzy sets," Information and Control, vol. 8, no. 3, pp. 338-353, 1995.

Publication Details

Published in : Volume 4 | Issue 7 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 505-519
Manuscript Number : IJSRSET184831
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Tigilu Mitiku, Mukhdeep Singh Manshahia, " Neuro Fuzzy Inference Approach : A Survey , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 7, pp.505-519, March-April-2018.
Journal URL : http://ijsrset.com/IJSRSET184831

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