Part 1 - Theoretical Foundations.- Interval Type-2 Fuzzy Logic Systems and Perceptual Computers: Their Similarities and Differences.- Continuous Karnik-Mendel Algorithms and Their Generalizations.- Two Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers: Adaptiveness and Novelty.- Interval Type-2 Fuzzy Markov Chains.- zSlices Based General Type-2 Fuzzy Sets and Systems.- Geometric Type-2 Fuzzy Sets.- Type-2 Fuzzy Sets and Bichains.- Type-2 Fuzzy Sets and Conceptual Spaces.- Part B- Type-2 Fuzzy Set Membership Function Generation.- Modeling Complex Concepts with Type-2 Fuzzy Sets: The Case of User Satisfaction of Online Services.- Construction of Interval type-2 fuzzy sets from fuzzy sets. Methods and applications.- Interval type-2 fuzzy membership function generation methods for representing sample data.- Part C - Applications.- ype-2 Fuzzy Logic in Image Analysis and Pattern Recognition.- Reliable Tool Life Estimation with Multiple Acoustic Emission Signal Feature Selection and Integration Based on Type-2 Fuzzy Logic.- A Review of Cluster Validation with an Example of Type-2 Fuzzy Application in R.- Type-2 Fuzzy Set and Fuzzy Ontology for Diet Application.
This book explores recent developments in the theoretical foundations and novel applications of general and interval type-2 fuzzy sets and systems, including: algebraic properties of type-2 fuzzy sets, geometric-based definition of type-2 fuzzy set operators, generalizations of the continuous KM algorithm, adaptiveness and novelty of interval type-2 fuzzy logic controllers, relations between conceptual spaces and type-2 fuzzy sets, type-2 fuzzy logic systems versus perceptual computers; modeling human perception of real world concepts with type-2 fuzzy sets, different methods for generating membership functions of interval and general type-2 fuzzy sets, and applications of interval type-2 fuzzy sets to control, machine tooling, image processing and diet. The applications demonstrate the appropriateness of using type-2 fuzzy sets and systems in real world problems that are characterized by different degrees of uncertainty.
Explores real world problems which examines the frameworks that enable handling different types and levels of uncertainty
Provides introductory materials for potential readers and providing justifications for the use of type-2 fuzzy set
Discusses the applications of type-2 fuzzy sets in text classification, intrusion detection, control systems and will compare the efficiency and performance of the systems with their counterparts implemented with type-1 fuzzy sets