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Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools 2021 ed. [Hardback]

  • Formāts: Hardback, 173 pages, height x width: 235x155 mm, weight: 465 g, 50 Illustrations, color; 6 Illustrations, black and white; XXI, 173 p. 56 illus., 50 illus. in color., 1 Hardback
  • Sērija : Studies in Fuzziness and Soft Computing 408
  • Izdošanas datums: 29-Apr-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030722791
  • ISBN-13: 9783030722791
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  • Hardback
  • Cena: 136,16 €*
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  • Formāts: Hardback, 173 pages, height x width: 235x155 mm, weight: 465 g, 50 Illustrations, color; 6 Illustrations, black and white; XXI, 173 p. 56 illus., 50 illus. in color., 1 Hardback
  • Sērija : Studies in Fuzziness and Soft Computing 408
  • Izdošanas datums: 29-Apr-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030722791
  • ISBN-13: 9783030722791
Citas grāmatas par šo tēmu:

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.


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.