The third edition of this textbook presents an updated approach to fuzzy sets and systems that can model uncertainty -- i.e., "type-2" fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this latest edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems - from type-1 to interval type-2 to general type-2 - in one volume. A new chapter describes recent advances in type-1 and type-2 rule based fuzzy systems, including explainable AI (XAI), machine learning, new parameterizations of membership functions, and a top-down approach to fuzzy systems, explaining the performance improvement potential for the hierarchy of fuzzy systems using rule partitions, type-3 fuzzy sets and systems, etc. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material.
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Jerry M. Mendel received the Ph.D. degree in Electrical Engineering from the Polytechnic Institute of Brooklyn, Brooklyn, NY. Currently, he is Emeritus Professor of Electrical Engineering at the University of Southern California in Los Angeles, where he worked for 44 years. He has published close to 600 technical papers and is author and/or co-author of 12 books, including Uncertain Rule-based Fuzzy Logic Systems: Introduction and New Directions (Prentice-Hall, 2001), Perceptual Computing: Aiding People in Making Subjective Judgments (Wiley & IEEE Press, 2010), Introduction to Type-2 Fuzzy Logic Control: Theory and Application (Wiley & IEEE Press, 2014) and Uncertain Rule-based Fuzzy Systems: Introduction and New Directions, 2nd ed. (Springer, 2017). He is a Life Fellow of the IEEE, a Distinguished Member of the IEEE Control Systems Society, and a Fellow of the International Fuzzy Systems Association and the Asia-Pacific AI Association. He was President of the IEEE Control Systems Society in 1986, a member of the Administrative Committee of the IEEE Computational Intelligence Society for nine years, and Chairman of its Fuzzy Systems Technical Committee and the Computing With Words Task Force of that TC. Among his awards are the 1983 Best Transactions Paper Award of the IEEE Geoscience and Remote Sensing Society, the 1992 Signal Processing Society Paper Award, the 2002 and 2014 Transactions on Fuzzy Systems Outstanding Paper Awards, a 1984 IEEE Centennial Medal, an IEEE Third Millenium Medal, a Fuzzy Systems Pioneer Award (2008) from the IEEE Computational Intelligence Society for fundamental theoretical contributions and seminal results in fuzzy systems, and the 2021 IEEE Lotfi A. Zadeh Pioneer Award for developing and promoting type-2 fuzzy logic. As of March 27, 2023, his publications have been cited (Google Scholar) more than 63,000 times, with an h-index of 100 and an i10-index of 320.
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The third edition of this textbook presents a further updated approach to fuzzy sets and systems that can model uncertainty - i.e., 'type-2' fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications, from time-series forecasting to knowledge mining to classification to control and to explainable AI (XAI). This latest edition again begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty, leading to type-2 fuzzy sets and systems. New material is included about how to obtain fuzzy set word models that are needed for XAI, similarity of fuzzy sets, a quantitative methodology that lets one explain in a simple way why the different kinds of fuzzy systems have the potential for performance improvements over each other, and new parameterizations of membership functions that have the potential for achieving even greater performance for all kinds of fuzzy systems. For hands-on experience, the book provides information on accessing MATLAB, Java, and Python software to complement the content. The book features a full suite of classroom material. 604 pp. Englisch. N° de réf. du vendeur 9783031353772
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides an updated approach to fuzzy sets and systems that can model uncertaintyIncludes a new chapter on recent advances in type-1 and type-2 rule based fuzzy systemsFeatures complete classroom material including end-of-chapter exercises,. N° de réf. du vendeur 873621314
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Buch. Etat : Neu. Explainable Uncertain Rule-Based Fuzzy Systems | Jerry M. Mendel | Buch | xxiii | Englisch | 2024 | Springer | EAN 9783031353772 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. N° de réf. du vendeur 126911004
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Buch. Etat : Neu. Neuware -The third edition of this textbook presents a further updated approach to fuzzy sets and systems that can model uncertainty ¿ i.e., ¿type-2¿ fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications, from time-series forecasting to knowledge mining to classification to control and to explainable AI (XAI). This latest edition again begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty, leading to type-2 fuzzy sets and systems. New material is included about how to obtain fuzzy set word models that are needed for XAI, similarity of fuzzy sets, a quantitative methodology that lets one explain in a simple way why the different kinds of fuzzy systems have the potential for performance improvements over each other, and new parameterizations of membership functions that have the potential for achieving even greater performance for all kinds of fuzzy systems. For hands-on experience, the book provides information on accessing MATLAB, Java, and Python software to complement the content. The book features a full suite of classroom material.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 604 pp. Englisch. N° de réf. du vendeur 9783031353772
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The third edition of this textbook presents a further updated approach to fuzzy sets and systems that can model uncertainty - i.e., 'type-2' fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications, from time-series forecasting to knowledge mining to classification to control and to explainable AI (XAI). This latest edition again begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty, leading to type-2 fuzzy sets and systems. New material is included about how to obtain fuzzy set word models that are needed for XAI, similarity of fuzzy sets, a quantitative methodology that lets one explain in a simple way why the different kinds of fuzzy systems have the potential for performance improvements over each other, and new parameterizations of membership functions that have the potential for achieving even greater performance for all kinds of fuzzy systems. For hands-on experience, the book provides information on accessing MATLAB, Java, and Python software to complement the content. The book features a full suite of classroom material. N° de réf. du vendeur 9783031353772
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