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Explainable neural networks based on...

Dombi, Jozsef.

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Explainable neural networks based on fuzzy logic and multi-criteria decision tools

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explainable neural networks based on fuzzy logic and multi-criteria decision tools/ by Jozsef Dombi, Orsolya Csiszar.
Author:	Dombi, Jozsef.
other author:	Csiszar, Orsolya.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xxi, 173 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-3-030-72280-7
ISBN:	9783030722807

Explainable neural networks based on fuzzy logic and multi-criteria decision tools
Dombi, Jozsef.

Explainable neural networks based on fuzzy logic and multi-criteria decision tools[electronic resource] /by Jozsef Dombi, Orsolya Csiszar. - Cham :Springer International Publishing :2021. - xxi, 173 p. :ill. (some col.), digital ;24 cm. - Studies in fuzziness and soft computing,v.4081434-9922 ;. - Studies in fuzziness and soft computing ;v.408..

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.

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.

ISBN: 9783030722807

Standard No.: 10.1007/978-3-030-72280-7doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .D663 2021

Dewey Class. No.: 006.32

Explainable neural networks based on fuzzy logic and multi-criteria decision tools
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505 0 $a 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.
520 $a 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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