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Preface; Contents; 1 Introduction; 2 Background and Theory; 2.1 Granular Computing; 2.2 Information Granule Representations; 2.3 Principle of Justifiable Granularity; 2.4 Data Granulation Algorithms; 2.5 Fuzzy Logic; 2.5.1 Type-1 Fuzzy Sets; 2.5.2 Type-2 Fuzzy Sets; 2.6 Fuzzy Granular Computing; References; 3 Advances in Granular Computing; 3.1 Fuzzy Granular Gravitational Clustering Algorithm; 3.2 Higher-Type Information Granule Formation; 3.2.1 A Hybrid Method for IT2 TSK Formation Based on the Principle of Justifiable Granularity and PSO for Spread Optimization

3.2.2 Information Granule Formation via the Concept of Uncertainty-Based Information with IT2 FS Representation with TSK Consequents Optimized with Cuckoo Search3.2.3 Method for Measurement of Uncertainty Applied to the Formation of IT2 FS; 3.2.4 Formation of GT2 Gaussian Membership Functions Based on the Information Granule Numerical Evidence; References; 4 Experimentation and Results Discussion; 4.1 Granulation Algorithms; 4.2 Higher-Type Information Granule Algorithms; 4.3 Application. General Type-2 Fuzzy Controller; References; 5 Conclusions; Appendix A; Appendix B; Outline placeholder

Appendix B.1Appendix B.2; Appendix B.3; Appendix B.4; Appendix B.5; Appendix C; Outline placeholder; Appendix C.1; Appendix C.2; Appendix C.3; Appendix C.4; Appendix C.5; Appendix C.6; Index

In this book, a series of granular algorithms are proposed. A nature inspired granular algorithm based on Newtonian gravitational forces is proposed. A series of methods for the formation of higher-type information granules represented by Interval Type-2 Fuzzy Sets are also shown, via multiple approaches, such as Coefficient of Variation, principle of justifiable granularity, uncertainty-based information concept, and numerical evidence based. And a fuzzy granular application comparison is given as to demonstrate the differences in how uncertainty affects the performance of fuzzy information granules.