Overview
- Presents the current state-of-the-art in Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools
- Presents recent research focusing on a special class of continuous-valued logic and multi-criteria decision tools
- Proposes a consistent framework for modeling human thinking by using the tools of both fields: fuzzy logical operators as well as multi-criteria decision tools, such as aggregative and preference operators
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 408)
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Table of contents (10 chapters)
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Front Matter
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Elements of Nilpotent Fuzzy Logic
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Front Matter
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Decision Operators
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Front Matter
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Learning and Neural Networks
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Front Matter
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Keywords
About this book
The research presented in this book shows how combining deep neural networks with a special class of fuzzy logical rules and multi-criteria decision tools can make deep neural networks more interpretable – and even, in many cases, more efficient.
Fuzzy logic together with multi-criteria decision-making tools provides very powerful tools for modeling human thinking. Based on their common theoretical basis, we propose a consistent framework for modeling human thinking by using the tools of all three fields: fuzzy logic, multi-criteria decision-making, and deep learning to help reduce the black-box nature of neural models; a challenge that is of vital importance to the whole research community.Authors and Affiliations
Bibliographic Information
Book Title: Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools
Authors: József Dombi, Orsolya Csiszár
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/978-3-030-72280-7
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-72279-1Published: 29 April 2021
Softcover ISBN: 978-3-030-72282-1Published: 29 April 2022
eBook ISBN: 978-3-030-72280-7Published: 28 April 2021
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XXI, 173
Number of Illustrations: 6 b/w illustrations, 50 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Complexity