Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction

Author:

Rong Hai-Jun,Sundararajan N.,Huang Guang-Bin,Saratchandran P.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Logic

Reference20 articles.

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2. P. Angelov, D. Filev, Simpl_eTS: a simplified method for learning evolving Takagi–Sugeno fuzzy models, in: The 14th IEEE Internat. Conf. on Fuzzy Systems (2005) 1068–1073.

3. P. Angelov, J. Victor, A. Dourado, D. Filev, On-line evolution of Takagi–Sugeno fuzzy models, in: Proc. Second IFAC Workshop on Advanced Fuzzy/Neural Control, Oulu, Finland, 2004, pp. 67–72.

4. Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction;Cho;Fuzzy Sets and Systems,1996

5. Evolving self-organizing maps for online learning, data analysis and modeling;Deng,2000

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