Titlebook: Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools; József Dombi,Orsolya Csiszár Book 2021 The Editor(s) (

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書目名稱Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools影響因子(影響力)




書目名稱Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools影響因子(影響力)學(xué)科排名




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書目名稱Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools被引頻次




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書目名稱Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools讀者反饋




書目名稱Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools讀者反饋學(xué)科排名

Lice692

https://doi.org/10.1007/978-981-287-633-1nt types of operators using only one generator function. The formula also contains a parameter with the semantical meaning of the threshold of expectancy. Interestingly, the resulting formula turns out to be equivalent to that used in current deep learning techniques.

兇殘

Interpretable Neural Networks Based on Continuous-Valued Logic and Multi-criteria Decision Operators

改變立場(chǎng)

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

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RACE

https://doi.org/10.1007/978-3-319-67122-2ng. The results of this chapter form the basis for constructing fuzzy logical systems that can later, in Chap.?9, be represented by neural-network transformations. This representation will assist the natural language interpretation of machine learning methods.

RACE

Henrique A. Almeida,Eunice S. G. Oliveiraations and the concept of a weak ordering property. Furthermore, we consider both R- and S-implications with respect to the three naturally derived negations from the previous chapter. The formulae and the basic properties of these implications are given which will come in handy when we implement fuzzy logic into neural architecture in Chap.?..

治愈

Henrique A. Almeida,Mário S. Correiaor applications in image processing, we define the overall equivalence of two grey level images and give an important semantic meaning of the aggregated equivalences. Finally, for applications in image processing, we define the overall equivalence of two grey level images and give an important semantic meaning to the aggregated equivalences.

自愛

Connectives: Conjunctions, Disjunctions and Negationsng. The results of this chapter form the basis for constructing fuzzy logical systems that can later, in Chap.?9, be represented by neural-network transformations. This representation will assist the natural language interpretation of machine learning methods.

混亂生活

Implicationsations and the concept of a weak ordering property. Furthermore, we consider both R- and S-implications with respect to the three naturally derived negations from the previous chapter. The formulae and the basic properties of these implications are given which will come in handy when we implement fuzzy logic into neural architecture in Chap.?..