國立虎尾科技大學 |

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools/ by József Dombi, Orsolya Csiszár.
Author:	Dombi, József.
other author:	Csiszár, Orsolya.
Description:	XXI, 173 p. 56 illus., 50 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
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, József.

Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools[electronic resource] /by József Dombi, Orsolya Csiszár. - 1st ed. 2021. - XXI, 173 p. 56 illus., 50 illus. in color.online resource. - Studies in Fuzziness and Soft Computing,4081434-9922 ;. - Studies in Fuzziness and Soft Computing,319.

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:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools
LDR:02455nam a22004095i 4500 001 1053032
003 DE-He213
005 20210623160731.0
007 cr nn 008mamaa
008 220103s2021 gw | s |||| 0|eng d
020 $a 9783030722807 $9 978-3-030-72280-7
024 7 $a 10.1007/978-3-030-72280-7 $2 doi
035 $a 978-3-030-72280-7
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Dombi, József. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1351022
245 1 0 $a Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools $h [electronic resource] / $c by József Dombi, Orsolya Csiszár.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XXI, 173 p. 56 illus., 50 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Fuzziness and Soft Computing, $x 1434-9922 ; $v 408
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.
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 0 $a Computational complexity. $3 527777
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Complexity. $3 669595
700 1 $a Csiszár, Orsolya. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1357849
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030722791
776 0 8 $i Printed edition: $z 9783030722814
776 0 8 $i Printed edition: $z 9783030722821
830 0 $a Studies in Fuzziness and Soft Computing, $x 1434-9922 ; $v 319 $3 1253810
856 4 0 $u https://doi.org/10.1007/978-3-030-72280-7
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)