Fuzzy Logic and Neuro-Fuzzy Modeling
S R Nikam, P J Nikumbh and S P Kulkarni. Article: Fuzzy Logic and Neuro-Fuzzy Modeling. IJCA Proceedings on National Conference on Recent Trends in Computing NCRTC(4):22-31, May 2012. Full text available. BibTeX
@article{key:article,
author = {S. R. Nikam and P. J. Nikumbh and S. P. Kulkarni},
title = {Article: Fuzzy Logic and Neuro-Fuzzy Modeling},
journal = {IJCA Proceedings on National Conference on Recent Trends in Computing},
year = {2012},
volume = {NCRTC},
number = {4},
pages = {22-31},
month = {May},
note = {Full text available}
}
Abstract
Fuzzy logic and fuzzy systems have recently been receiving a lot of attention; both from the media and scientific community, yet the basic techniques were originally developed in the mid-sixties. Fuzzy logic provides a formalism for implementing expert or heuristic rules on computers, and while this is the main goal in the field of expert or knowledge-based systems, fuzzy systems have had considerably more success and have been sold in automobiles, cameras, washing machines, rice cookers, etc. This report will describe the theory behind basic fuzzy logic and investigate how fuzzy systems work. This leads naturally on to neuro fuzzy systems which attempt to fuse the best points of neural and fuzzy networks into a single system. Throughout this report, the potential limitations of this method will be described as this provides the reader with a greater understanding of how the techniques can be applied.
References
- Ajith Abraham, "Neuro Fuzzy Systems: state of Art Modelling Techniques", In proceedings of the sixth international work conference on Artificial and Natural Neural Networks, IWANN 2001, Granada, Springer Verlag Germany, pp. 269-276, June 2001.
- Ajith Abraham & Baikunth Nath, "Hybrid intelligent systems design- A review of a decade of research", School of computing & information technology, Monash University, Australia, technical report series 5/2000, pp. 1-55, 2000.
- A. Abraham, "Adaptation of Fuzzy Inference System Using Neural Learning", Computer Science Department, Oklahoma State University, USA, springer verlag berlin Heidelberg, 2005.
- Heikki Koivo1, "ANFIS (Adaptive Neuro-Fuzzy Inference System)" ??2000
- Jang R, "Neuro-Fuzzy Modelling: Architectures, Analyses and Applications", PhD Thesis, University of California, Berkeley, July 1992.
- Jyh-Shing Roger Jang, "ANFIS: Adaptive network based fuzzy inference system", University of California, Berkeley, CA 91720,IEEE transaction on systems, Man, and Cybernetics, 23(03):665-685, May 1993.
- Jyh-Shing Roger Jang, Chuen-Tsai Sun, "Neuro Fuzzy Modelling and Control", Proceedings of IEEE, vol. 83, pp. 378-406, 1995.
- Jan Jantzen ,"Neurofuzzy Modelling", an computational approach to intelligence, 1995.
- José Vieira,Fernando, Morgado Dias, Alexandre Mota, "Neuro-Fuzzy Systems: A Survey", 5th WSEAS NNA International Conference on Neural Networks and Applications, Udine, Italia, 2004.