Explainable neural networks based on fuzzy logic and multi-criteria decision tools
Author(s)
Bibliographic Information
Explainable neural networks based on fuzzy logic and multi-criteria decision tools
（Studies in fuzziness and soft computing, v. 408）
Springer, c2021
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Includes bibliographical references
Description and Table of Contents
Description
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.
Table 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.
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