Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools

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.

List of contents

Chapter 1: Connectives: Conjunctions, Disjunctions and Negations.- Chapter 2: Implications.- Chapter 3: Equivalences.- Chapter 4: Modifiers and Membership Functions in Fuzzy Sets.- Chapter 5: Aggregative Operators.- Chapter 6:  Preference Operators.

Product details

• Authors: Orsolya Csiszár, József Dombi
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 01.01.2022
• EAN: 9783030722821
• ISBN: 978-3-0-3072282-1
• No. of pages: 173
• Dimensions: 155 mm x 10 mm x 235 mm
• Illustrations: XXI, 173 p. 56 illus., 50 illus. in color.
• Series: Studies in Fuzziness and Soft Computing
• Subject: Natural sciences, medicine, IT, technology > Technology > General, dictionaries