This article is about explainable artificial intelligence (XAI) for rule-based fuzzy systems [that
can be expressed generically, as]. It explains why it is not valid to explain the output of …

A deep convolutional fuzzy system (DCFS) on a high-dimensional input space is a multilayer
connection of many low-dimensional fuzzy systems, where the input variables to the low …

This study proposes a novel methodology for the extraction of a hierarchical Takagi-Sugeno
fuzzy rule-based architecture from data. This architecture reduces the number and …

As an important branch of fuzzy systems, a hierarchical fuzzy system (HFS) has a wide
range of applications in system science, medical science, and engineering. Using the …

Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve the
interpretability of fuzzy logic systems (FLSs). However, challenges remain, such as “How …

Analysing the interpretability of a fuzzy system (either hierarchical or not) involves
consideration of its semantic properties and evaluation of its structural complexity. The …

Heterogeneous big data poses many challenges in machine learning. Its enormous scale,
high dimensionality, and inherent uncertainty make almost every aspect of machine learning …

This book introduces the novel concept of a fuzzy network. In particular, it describes further
developments of some results from its predecessor book on Complexity Management in …

Hierarchical fuzzy systems (HFSs) have been shown to have the potential to improve
interpretability of fuzzy logic systems (FLSs). In recent years, a variety of indices have been …

In this paper, adaptive fuzzy tracking control is investigated for a class of high-order switched
nonlinear systems. The considered systems possess the characteristic of completely …