Название: An Introduction to Computing with Fuzzy Sets: Analysis, Design, and ApplicationsАвтор: Witold Pedrycz
Издательство: Springer
Год: 2020
Страниц: 287
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
This book provides concise yet thorough coverage of the fundamentals and technology of fuzzy sets. Readers will find a lucid and systematic introduction to the essential concepts of fuzzy set-based information granules, their processing and detailed algorithms. Timely topics and recent advances in fuzzy modeling and its principles, neurocomputing, fuzzy set estimation, granulation–degranulation, and fuzzy sets of higher type and order are discussed. In turn, a wealth of examples, case studies, problems and motivating arguments, spread throughout the text and linked with various areas of artificial intelligence, will help readers acquire a solid working knowledge. Given the book’s well-balanced combination of the theory and applied facets of fuzzy sets, it will appeal to a broad readership in both academe and industry. It is also ideally suited as a textbook for graduate and undergraduate students in science, engineering, and operations research.
The pursuits in data mining, data analytics, image understanding and interpretation, recommender systems, explainable artificial intelligence (XAI) and inherently associated with human centricity are at the forefront of advanced technologies being under intensive studies. Fuzzy sets have been evolving over the half century fostering new interactions and exploring uncharted territories within the discipline of computational intelligence. With this regard, fuzzy sets are important examples of information granules. Granular computing offers processing principles that opens new frontiers in computing with fuzzy sets and builds an overarching and conceptually appealing
processing environment.
In the recent years, Artificial Intelligence has emerged as an important, timely, and far reaching research discipline with a plethora of advanced and highly visible applications. With the rapid progress of concepts and methods of AI, there is a recent and vivid trend to augment the paradigm by bringing aspects of explainability. With the ever growing complexity of AI constructs their relationships with data analytics (and inherent danger of cyberattacks and the presence of adversarial data) and the omnipresence of demanding applications in various criteria domains, there is a growing need to associate the results with sound explanations All of these factors have given rise to the most recent direction of Explainable AI (XAI for brief) to foster the developments of XAI architectures and augment AI with the facets of human centricity, which becomes indispensable.
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