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.

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

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