AND ITS APPLICATION by YU QIU Under the Direction of Yanqing Zhang ABSTRACT In recent years, the type-2 fuzzy sets theory has been used to model and minimize the effects of uncertainties in rule-base fuzzy logic system. In order to make the type-2 fuzzy logic system reasonable and reliable, a new simple and novel statistical method to decide interval-valued fuzzy membership functions and a new ...

in this paper, we present a revised similarity measure based onchen-and-chen's similarity measure for fuzzy risk analysis. the revisedsimilarity measure uses the corrected formulae to calculate the centre ofgravity points, therefore it is more  effective than the chen-and-chen'smethod. the revised similarity measure can overcome the drawbacks of theexisting methods. we have also proposed a new ...

Type-1 Fuzzy Logic Systems (FLSs) have been applied to date with great success to different applications. However, for many real-world applications, there is a need to cope with large amounts of uncertainties. The traditional Type-1 FLSs that use crisp Type-1 fuzzy sets cannot directly handle such uncertainties. Type-2 FLSs that use Type2 fuzzy sets can handle such uncertainties to produce a be...

Here, an Indirect Adaptive Interval Type-2 Fuzzy Sliding Mode Control (AIT2FSMC) is introduced for a class of nonlinear systems. In order to approximate unknown nonlinear functions, interval type-2 fuzzy logic system (IT2FLS) can be useful in presence of noisy data and external disturbances. Since, adjusting the interval type-2 fuzzy Membership Functions (MFs) to improve performance is a diffic...

Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties and to perform under noisy conditions in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for interval type-2 Takagi–Sugeno–Kang (TSK) fuzzy neural networks is proposed in this paper. Differently from the other rece...

This paper describes an interval type-2 fuzzy modeling framework, reduced-set vector-based interval type-2 fuzzy neural network (RV-based IT2FNN), to characterize the representation in fuzzy logic inference procedure. The model proposed introduces the concept of interval kernel to interval type-2 fuzzy membership, and provides an architecure to extract reduced-set vectors for generating interva...

Planning resources for a supply chain is a major factor determining its success or failure. In this paper we build on previous work introducing an Interval Type-2 Fuzzy Logic model of a multiple echelon supply chain. It is believed that the additional degree of uncertainty provided by Interval Type-2 Fuzzy Logic will allow for better representation of the uncertainty and vagueness present in re...