Due to its abilities, such as handling imprecisions, uncertainties and large data sets, adaptive neuro-fuzzy inference system (ANFIS) is evolved to be one of the commonly used techniques.
Zadeh [45] proposed the concept of fuzzy set theory and successfully used to handle imprecision (or uncertainty) in decision making problems, to solve the ambiguity and vagueness in information from human judgements and preferences.
Nonlinear effects are included in the structural formulations and a fuzzy methodology is adopted for handling the imprecision present in the structure modeling.
The trapezoidal and the triangular extent fuzzy AHP methods are employed to handle the imprecision and uncertainty of the effects of factors.
Thus, we have designed a method to handle this imprecision, through establishing a temporal constraint that is called Fuzzy Temporal Window.
And fuzzy logic technology seems suitable to handle the imprecision problems of the practical wireless network [29].
A mathematical modelling of a real world phenomenon, very often, leads to a difference equation and on the other hand, Fuzzy Logic can handle uncertainness, imprecision or vagueness related to the experimental input-output data.
This provides a high order intuitive mechanism for representing imperfect information by means of use of fuzzy If Then rules, in addition to handling uncertainty and imprecision.
Aiming at handling the track imprecision caused by inertial lag, model uncertainties and atmospheric environment disturbances, as well as stochastic noises, a terminal guidance law based on stochastic fast smooth second-order sliding modes control theory is proposed.
A comparative analysis with an Optimal Type-1 Fuzzy Logic Controller (OT1FLC) and a PI controller, demonstrated OT2FLC׳s superiority; which is evident in handling uncertainty and imprecision induced in the system by means of noise and disturbances.
