The uncertain nonlinear systems can be modeled with fuzzy equations by incorporating the fuzzy set theory. In this paper, the fuzzy equations are applied as the models for the uncertain nonlinear systems. The nonlinear modeling process is to find the coefficients of the fuzzy equations. We use the neural networks to approximate the coefficients of the fuzzy equations. The approximation theory for crisp models is extended into the fuzzy equation model. The upper bounds of the modeling errors are estimated. Numerical experiments along with comparisons demonstrate the excellent behavior of the proposed method.
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Publication date: 18 April 2017 Source: Cell Reports, Volume 19, Issue 3 Author(s): David Estoppey, Chia Min Lee, Marco Janoschke, Boon He...
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Radiation Research, Volume 187, Issue 6 , Page 647-658, June 2017. from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader ...
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Publication date: Available online 23 February 2017 Source: Journal of Biomechanics Author(s): Lipika Parida, Udita Uday Ghosh, Venkat Pad...
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