Considering the benefits of the human decision making, the efforts have been executed to
implement it in machines. The chronic problem addressed in this implementation is the …

This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various
applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information …

Most real world applications face high levels of uncertainties that can affect the operations of
such applications. Hence, there is a need to develop different approaches that can handle …

A proposed learning methodology based on a hybrid mechanism for training interval A2-C1
type-2 non-singleton type-2 Takagi–Sugeno–Kang fuzzy logic systems uses a recursive …

It has been demonstrated that type-2 fuzzy logic systems are much more powerful tools than
ordinary (type-1) fuzzy logic systems to represent highly nonlinear and/or uncertain systems …

This paper proposes a new interval type-2 fuzzy set taking extended π interval type-2
membership function (IT2 MF) as its values, and presents a new procedure for generating a …

Interval type-2 fuzzy sets (IT2 FSs) can model and cope with the uncertainties existing in the
real-world applications. There exist several uncertainty measures for IT2 FSs, such as …

This chapter explores many aspects of the interval type-2 (IT2) fuzzy system that was
introduced in Chap. 1. As was done for type-1 (T1) fuzzy systems, it provides a very …

The prior knowledge-monotonicity property-is helpful for system analysis, modeling and
design, especially when no specific physical structure knowledge about systems is …

A type‐2 fuzzy logic system (FLS) can handle numerical and linguistic uncertainties, but, like
a type‐1 FLS, rule explosion is one of its major disadvantages. In this paper, we present a …