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Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools
Título:
Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools
ISBN:
9783030722807
Autor personal:
Edición:
1st ed. 2021.
PRODUCTION_INFO:
Cham : Springer International Publishing : Imprint: Springer, 2021.
Descripción física:
XXI, 173 p. 56 illus., 50 illus. in color. online resource.
Serie:
Studies in Fuzziness and Soft Computing, 408
Contenido:
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.
Síntesis:
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.
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