[PDF] Top 20 Interval type 2 defuzzification using uncertainty weights

... the uncertainty of the right Gaussian is a little smaller that the uncertainty of the left ...different defuzzification results are marked by vertical ...higher defuzzification result which ... See full document

... between interval type-2 fuzzy sets and fiber bundles, it is advisable, at first, to observe the FOU (Footprint Of Uncertainty) of a typical interval type-2 fuzzy set, as ... See full document

... An interval type-2 fuzzy logic approach may help to diagnose induction motor ...described using linguistic variables. An interval type-2 fuzzy logic system (IT2FLS), which ... See full document

... handle uncertainty by taking into account both the membership and non-membership degrees of an element x to a fuzzy set A together with extra degree of indeterminacy ... See full document

... under uncertainty and the consideration of risk. Type-1 fuzzy logic by its (essentially) crisp nature is limited in modelling decision making as there is no uncertainty in the membership ...that ... See full document

... to uncertainty to help design type-2 fuzzy logic sys- tems, including Wu and Mendel in [11] who present a technique in which forms of uncertainty are defined and it is investigated how these ... See full document

... precise Type-1 fuzzy sets. For example, consider a Type-1 fuzzy set representing the linguistic label of ‘Low’ voltage in ...this Type-1 fuzzy set can vary due to uncertainties (which could arise for ... See full document

... in type-2 fuzzy sets. Chen and Lee [2] developed their method using type-2 TOPSIS ...ranking interval type-2 fuzzy sets in order to deal with fuzzy multiple ... See full document

... of type-2 fuzzy rules to handle the uncertainty of some linguistic ...by using different words like numbness, tingling, traveling pain and other symptoms of degenerative disc disease, the ... See full document

... closed-form type reduction and defuzzification methods which is adapted to design the type-reduction parameters as well as stability of the IT2-TSK fuzzy ... See full document

... Liang and Mendel [5] introduced the theory and design procedures of Interval Type-2 Fuzzy Logic Systems (IT2 FLS). The IT2 FLS has always been considered to be a special case of a general T2 FLS; ... See full document

... on interval type 2 fuzzy logic (IT2FL), a signal processing of the radio signal strength indicator (RSSI) minimizes the uncertainty in RSSI measurements from anchors caused by the indoor ...of ... See full document

... Impedance control is basically defined as a torque control scheme. In the other words, the joint torques of robot are commended to implement impedance control. For this purpose, the controller should overcome complex ... See full document

... of uncertainty (FOU) to our T2 fuzzy logic, we make simulations as follows: We tested T1 fuzzy logic and T2 fuzzy logic for different spread or (FOU), from the same initial position and for the same number of ... See full document

... fire using automated sensors definitely requires efficient and accurate ...fuzzy type-2 logic for fire ...an uncertainty about how much extent of a factor like temperature, humidity and light ... See full document

... a type–2 fuzzy set, without type reduction to a type–1 fuzzy ...NT type reduction yields a (type–1) membership function that passes right through the middle of the grey area ... See full document

... developed using the type-2 fuzzy logic ...the type-2 FLSs the membership function itself is ...handling uncertainty, which is certainly encountered in noisy ...Hence ... See full document

... The most important statistical estimator for assessing the multi-regression model is the standard error of the estimate SEE. For the upper threshold value we took the testing facilities’ measurement uncertainty of ... See full document

... However, as noted by Lavie et al. (2008), Liu et al. (2009), and Chiang (2010), the integration of syntactic information on both sides tends to de- crease translation quality because the systems be- come too restrictive. ... See full document

... Parallelization has successfully reduced the computation time of the QoWS monitoring models. This can be seen from all types of FIS models, namely Mamdani, Sugeno-Average and Sugeno-Sum. Sugeno FIS models outperformed ... See full document