Interval Type-2 Fuzzy Systems: Design Methods and Case Studies

This chapter is the IT2 version of Chap. 4 . It focuses first on what exactly “design of an IT2 fuzzy system” means, then provides a tabular way for making the choices that are needed in order to fully specify an IT2 fuzzy system, introduces two approaches to design—the partially dependent approach and the totally independent approach (but this time for singleton, T1 non-singleton, and IT2 non-singleton IT2 fuzzy systems), includes some important design methods and has five extensive case studies. The coverage of this chapter includes: a detailed overview of what designing IT2 fuzzy systems means; the extension of seven of the design methods that were covered for designing type-1 fuzzy systems in Chap. 4 , to IT2 fuzzy systems, namely: IT2 WM, clustering using fuzzy c-means, least squares, derivative-based (back-propagation), derivative-free (QPSO), and hybrid (adaptive network fuzzy inference system—ANFIS, and structure identification and feature extraction—SIFE—for TSK systems); and the continuation of the Chap. 4 five case studies from T1 to IT2 fuzzy systems (forecasting of time series, knowledge mining using surveys, rule-based classification of video traffic, fuzzy logic control, and explainable IT2 fuzzy systems). The appendix introduces novel Constraints Almost Always Satisfied Parameters (CAASPs) for IT2 fuzzy set and systems. Eleven examples are used to illustrate the chapter’s important concepts.