Titlebook: Repetitorium Internistische Intensivmedizin; Guido Michels,Matthias Kochanek Book 20101st edition Springer-Verlag Berlin Heidelberg 2010 A

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發(fā)誓放棄

, 26, 30, 34]). The learning of this type of fuzzy models is, in general, done by dividing the learning process into two subtasks which are solved independently from each other: the construction of the reference fuzzy sets (fuzzy partition of the input and output spaces) on the one hand, and on the

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Guido Michelsough the application of fuzzy models proved to be effective for the approxima- tion of uncertain nonlinear processes, the data-driven identification offuzzy models alone sometimes yields complex and unrealistic models. Typically, this is due to the over-parameterization of the model and insufficient

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確認(rèn)

Matthias Kochanek,Guido Michelshem in construction of own algorithms.Problem vizualization In the last ten years, a true explosion of investigations into fuzzy modeling and its applications in control, diagnostics, decision making, optimization, pattern recognition, robotics, etc. has been observed. The attraction of fuzzy modeli

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Guido Michels,Matthias Kochanekel. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that TS identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the TS method because this type of members

嚴(yán)峻考驗

Guido Michelsel. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that TS identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the TS method because this type of members

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