Jerry M. Mendel
Explainable Uncertain Rule-Based Fuzzy Systems
Explainable Uncertain Rule-Based Fuzzy Systems
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- More about Explainable Uncertain Rule-Based Fuzzy Systems
The third edition of this textbook presents an updated approach to fuzzy sets and systems that can model uncertainty, enabling a wide range of applications. It covers type-1 and type-2 fuzzy sets, similarity of fuzzy sets, a quantitative methodology, and new parameterizations of membership functions. It also provides hands-on experience with MATLAB, Java, and Python software.
Format: Hardback
Length: 580 pages
Publication date: 02 February 2024
Publisher: Springer International Publishing AG
The third edition of this textbook takes a comprehensive and up-to-date approach to fuzzy sets and systems, enabling them to model uncertainty effectively. The author elucidates how to surpass the limitations of classical fuzzy sets and systems, thereby broadening their applications to diverse fields such as time-series forecasting, knowledge mining, classification, control, and explainable AI (XAI). This latest edition begins by introducing classical (type-1) fuzzy sets and systems, and then proceeds to demonstrate how they can be modified to handle uncertainty, leading to the development of type-2 fuzzy sets and systems.
In addition to covering the fundamentals, the book delves into new topics such as obtaining fuzzy set word models for XAI, exploring the similarity of fuzzy sets, employing a quantitative methodology to explain the potential performance advantages of different types of fuzzy systems, and introducing novel parameterizations of membership functions that can further enhance the performance of all fuzzy systems.
To facilitate hands-on learning, the book provides access to MATLAB, Java, and Python software, complementing the theoretical content. Furthermore, it includes a comprehensive suite of classroom materials, making it an invaluable resource for students and practitioners alike.
By embracing the power of fuzzy sets and systems, this textbook empowers individuals to navigate the complexities of uncertainty and unlock the full potential of data-driven decision-making in various industries.
Weight: 1705g
Dimension: 279 x 210 (mm)
ISBN-13: 9783031353772
Edition number: 3rd ed. 2024
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