Reply to comments provided by Z. Şen on "Takagi-Sugeno fuzzy system for modeling stage-discharge relationship" by A.K. Lohani, N.K. Goel and K.K.S. Bhatia

Journal of Hydrology

Volumn 337, Issue 1-2, 2007, Pages 244-247

  Lohani, A K ^a   Goel, N K ^b   Bhatia, K K S ^a  

^a NATIONAL INSTITUTE OF HYDROLOGY   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

Author keywords

Cluster center; Cluster radius; Discussion; Subtractive clustering; Takagi Sugeno model

Indexed keywords

EID: 34047140783     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2007.01.032     Document Type: Letter

References (17)

1. Angelov, P., Buswll, R., 2001. Evolving rule based models: A tool for intelligent adaptation. IEEE 1062-1067.
2. Chiu S.L. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy System 2 (1994) 267-278
3. Deka P., and Chandramouli V. A fuzzy neural network model for deriving the river stage-discharge relationship. Hydrological Science Journal 48 2 (2003) 197-209
4. Kisi O. Daily suspended sediment modeling using a fuzzy-differential evolution approach. Hydrological Sciences Journal 49 1 (2004) 183-197
5. Kisi O. Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrological Sciences Journal 50 4 (2005) 683-696
6. Kisi, O., Karahan, M.E., Şen, Z., 2006. River suspended sediment modeling using fuzzy logic approach. Hydrolological Processes (http://www3.interscience.wiley.com/cgi-bin/jissue/89013836?CRETRY= 1&SRETRY=0), doi:10.1002/hyp.6166.
7. Lohani A.K., Goel N.K., and Bhatia K.K.S. Takagi-Sugeno fuzzy inference system for modeling stage-discharge relationship. Journal of Hydrology 331 (2006) 146-160
8. MATLAB 6.5, 2002. The Math Works, Inc., 3 Apple Hill Drive Natick, MA 01760-2098.
9. Nash J., and Sutcliffe J. River flow forecasting through conceptual models part I - a discussion of principles. Journal of Hydrology 10 (1970) 282-290
10. Nayak P.C., Sudheer K.P., and Ramasastri K.S. Fuzzy computing based rainfall-runoff model for real time flood forecasting. Hydrological Processes 19 (2005) 955-968
11. Nayak, P.C., Sudheer, K.P., Rangan, D.M., Ramasastri, K.S., 2005b. Short-term flood forecasting with a neurofuzzy model. Water Resources Research (41), w04004, doi:10.1029/2004WR003562.
12. Paiva R.P., and Dourado A. Interpretability and learning in neuro-fuzzy systems. Fuzzy Sets and Systems 147 (2004) 17-38
13. Takagi T., and Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Systems, Man and Cybernetics 15 1 (1985) 116-132
14. Vernieuwe H., Georgieva O., Baets B.D., Pauwels V.R.N., Verhoest N.E.C., and Troch F.P.D. Comparison of data-driven Takagi-Sugeno models of rainfall-discharge dynamics. Journal of Hydrology 302 (2005) 173-186
15. Wang, H., Xiao, J., 2004. T-S fuzzy system based on multi-resolution analysis and its function approximation. In: Proceedings of the 5th World Congress on Intelligent Control and Automation, June 15-19, Hangzhou, PR China, IEEE, pp. 244-249.
16. Xiong L., Shamseldin A., and O'Connor K. A non-linear combination of the forecasts of rainfall-runoff models by the first order Takagi-Sugeno fuzzy system. Journal of Hydrology 245 (2001) 196-217
17. Zadeh L.A. Some reflections on the anniversary of fuzzy sets and systems. Fuzzy Sets and Systems 100 (1998) 5-7