In this paper we focus on model-based statistical signal processing and how some problems that are associated with it can be solved using fuzzy logic. We explain how uncertainty (which is prevalent in statistical signal processing applications) can be handled within the framework of fuzzy logic. Type-1 singleton and non-singleton fuzzy logic systems (FLSs) are reviewed. Type-2 FLSs, which are r...

Airports and their related operations have become the major bottlenecks to entire air traffic management system, raising predictability, safety, environmental concerns. One of underpinning techniques for digital sustainable transport is airport ground movement optimization. Currently, real data made freely available majority aircraft at many airports. However, recorded not accurate enough due m...

In many contexts, type-2 fuzzy sets (T2 FS) are obtained from a type-1 set to which we wish add uncertainty. However, in the current representation, there is no restriction on shape of footprint uncertainty and embedded (ESs) that can be considered acceptable. This leads, usually, loss semantic relationship between T2 FS concept it models. As consequence, interpretability some ESs explainabilit...

This chapter maps out the development of the PSO based Functional Link Interval Type-2 Fuzzy Neural System (FLIT2FNS) model used to forecast the stock market indices. In the process, it discusses the architecture of Functional Link Artificial Neural Network (FLANN), FLANN & Type-1Fuzzy Logic System (Type1FLS) and the differences between Type-1FLS and Interval Type-2 Fuzzy Logic System (IT2FLS)....