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By using the level-cut method, the fuzzy parameters are equivalently decomposed into interval variables.
In the proposed method, the fuzzy theory is employed to represent uncertainties involved in prediction.
The personalized service level is important for improving the service quality of library, therefore it is necessary to evaluate it based on effective method, the fuzzy analytic hierarchy is applied in it.
Comparisons were made for three technologies of regression analyses, including the conventional least squares regression method, the fuzzy linear regression method, and the newly derived fuzzy goal regression method.
According to the proposed design method, the fuzzy controller is carried out by parallel distributed compensation (PDC) concept to guarantee the robust asymptotical stability and attenuation performance of system in the sense of mean square.
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Using fuzzy inference methods, the Fuzzy PID parameters can be adaptively adjusted on line for varying state of the system and changing operating condition.
To improve the robustness to noise, the spatial information is taken into account and many modified versions of the Otsu method [13], the fuzzy entropy-based method [14, 15], the fuzzy clustering method [16 18], and the Renyi entropy-based method [19, 20] have emerged.
The second category is based on clustering algorithm, such as Otsu's method and the fuzzy clustering method.
An example is used to illustrate the flexibility and effectiveness of the proposed physical programming approach over the single-objective method and the fuzzy optimization method.
The fuzzy Ritz method and the fuzzy finite element method are presented as the applications of the fuzzy variational principle.
The proposed method uses the Fuzzy Finite Element Method (FFEM) to calculate fuzzy solutions (namely displacements and frequencies) and genetic algorithms for exploring the design space of fuzzy design variables.
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