Research Article
Solving Uncertain Problems using ANFIS
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 29 - Issue 11 |
| Published: September 2011 |
| Authors: Dr G.S.V.P.Raju, V.Mary Sumalatha, K.V.Ramani, K.V.Lakshmi |
10.5120/3690-5152
|
Dr G.S.V.P.Raju, V.Mary Sumalatha, K.V.Ramani, K.V.Lakshmi . Solving Uncertain Problems using ANFIS. International Journal of Computer Applications. 29, 11 (September 2011), 14-21. DOI=10.5120/3690-5152
@article{ 10.5120/3690-5152,
author = { Dr G.S.V.P.Raju,V.Mary Sumalatha,K.V.Ramani,K.V.Lakshmi },
title = { Solving Uncertain Problems using ANFIS },
journal = { International Journal of Computer Applications },
year = { 2011 },
volume = { 29 },
number = { 11 },
pages = { 14-21 },
doi = { 10.5120/3690-5152 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2011
%A Dr G.S.V.P.Raju
%A V.Mary Sumalatha
%A K.V.Ramani
%A K.V.Lakshmi
%T Solving Uncertain Problems using ANFIS%T
%J International Journal of Computer Applications
%V 29
%N 11
%P 14-21
%R 10.5120/3690-5152
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Uncertain problems are problems that have no definitive way of solving. Many of the uncertain problems come under intelligence systems that exhibit the characteristics we associate with intelligence in human behavior. Soft Computing[6] techniques which have drawn their inherent characteristics from biological systems, present an effective method for solving of even difficult inverse problems. The guiding principle of soft computing is to exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low cost solution, employment of soft computing for the solution of machine learning problems lead to high machine intelligence quotient. Hybrid intelligent systems deal with the integration of two or more of the technologies. The combined use of technologies has resulted in effective problem solving in comparison with each technology used individually and exclusively. The purpose of the paper is to solve an engineering problem, power failures in personal computers using neuro fuzzy modeling system ANFIS.
References
- Klir,G.J. and Yuan,B.(2000).Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice-Hall, India
- Kukolj,D.(2000).Design of adaptive Takagi-Sugeno-Kang Fuzzy models.Appl.Soft.Comput.,2, 89-103
- Rutkowaska,D.(2000).Neuro_fuzzy Architectures and Hybrid Learning. Physica-Verlag,U.S.A.
- Wang, Z.Y., “Artificial Intelligence Applications in the diagnosis of Power Transformer Incipient Faults”, Ph.D Dissertation submitted to Virginia Polytechnic Institute and State University, August 2000.
- W. Pedrycz and F. Gomide, Fuzzy Systems Engineering Towards Human-Centric Computing, John Wiley & Sons, New Jersey, 2007
- J.S.R.Jang, C.T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall, New jersey, 1997.
- D. Nauck, F. Klawonn, R. Kruse, Foundations of Neuro-Fuzzy Systems, Wiley, England, 1997.
- Yong Liu, Chanan Singh, Evaluation of the Failure Rates of transmission Lines During Hurricanes Using a Neuro –Fuzzy System, submitted to the 11th International IEEEConference PMAPS, 2010, Pages 569-574
- File: PowerOutageBlackout.svg, available at http://en.Wikipedia.org/wiki/File:PowerOutageBlackout.svg
- File:Dropout.svg, available at http://en.Wikipedia.org/wiki/File:Dropout.svg
- Rathinam, A.; Padmini, S.; Composite Counter propagation Neural Networks for Solving Power Flow Problem, Conference on Computational Intelligence and Multimedia Applications. 2007, Volume 1, Digital Object Identifier: 10.1109/ICCIMA.2007.348, Pages 212-216
- Rathinam, A.; Padmini, S.; Ravikumar, V.; Application of supervised learning artificial neural networks
- CPNN, BPNN for solving power flow problem. Information and Communication Technology in Electrical Sciences. 2007, ICTES, IET_UK International Conference, Pages 156-160.
Index Terms
Keywords