The method is illustrated, validated, and compared against three different fuzzy MCDM methods (fuzzy VIKOR, fuzzy TOPSIS, and fuzzy ELECTRE) in terms of its ranking performance.
In the quantitative analysis, the imprecise and vague information regarding the system failure behavior is quantified by using the principles of fuzzy mathematics in terms of fuzzy and crisp values.
In computer simulations, we compare four types of connection weights (i.e., real numbers, symmetric triangular fuzzy numbers, asymmetric triangular fuzzy numbers, and asymmetric trapezoidal fuzzy numbers) in terms of the fitting ability to training data and the computation time.
Some sufficient conditions for stochastic stability and stabilization conditions with a mode-dependent fuzzy controller are derived for the Markovian jump fuzzy systems in terms of linear matrix inequalities (LMIs).
The proposed adaptive controller outperforms the existing fuzzy controller in terms of steady state error, rise time and settling time.
Moreover, performance of the fuzzy controller in terms of decay rate and constraint on the control input is studied and LMI conditions for these performance criteria are obtained.
Also, the fuzzy derivative, in terms of level-cuts, is bigl[f'(x bigr]^{alpha}=bigl[minbigl{ underline{f}_{alpha}'(x),bar{f}_{alpha}'(x bigr},max bigl{ underline{f}_{alpha}'(x), bar{f}_{alpha}'(x bigr} bigr].
The representations have the advantage of allowing flexible and easy to control shapes of the fuzzy numbers (we use the standard α-cuts setting, but also the membership functions are obtained immediately) and can be used directly to obtain error-controlled-approximations of the fuzzy calculus in terms of a finite set of parameters.
Table 7 Performance of the multi-iris fuzzy vault in terms of GMR, FMR, FA-security, GDT, and IDT in relation to the polynomial degree k, applying the LG feature extraction with w=16 (top) and w=32 (bottom) using left and right eyes of the IITDv1 database k GMR FMR FA-sec.
