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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Background Although pneumonia is a leading cause of death in New York City (NYC), limited data exist about the settings in which pneumonia ...
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Summary We tested whether prophylactic droperidol and ondansetron, in combination with a moderate dose of dexamethasone, were equally effe...
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by Demin Li, Carol Bentley, Jenna Yates, Maryam Salimi, Jenny Greig, Sarah Wiblin, Tasneem Hassanali, Alison H. Banham Therapeutic monoclon...
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ACS Nano DOI: 10.1021/acsnano.6b08567 from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/2oNpdhD via...
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Abstract Dermoscopy has demonstrated clinical benefits in improving early melanoma diagnosis and reducing unnecessary biopsies. Despite th...
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