標題: Titlebook: Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools; József Dombi,Orsolya Csiszár Book 2021 The Editor(s) ( [打印本頁] 作者: counterfeit 時間: 2025-3-21 16:19
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)學科排名
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書目名稱Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools網絡公開度學科排名
書目名稱Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools被引頻次
書目名稱Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools被引頻次學科排名
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書目名稱Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools年度引用學科排名
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讀者反饋學科排名
作者: Lice692 時間: 2025-3-21 21:30
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.作者: 兇殘 時間: 2025-3-22 04:02
Interpretable Neural Networks Based on Continuous-Valued Logic and Multi-criteria Decision Operators作者: 改變立場 時間: 2025-3-22 06:25
Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools作者: Insufficient 時間: 2025-3-22 09:24 作者: RACE 時間: 2025-3-22 13:13
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.作者: RACE 時間: 2025-3-22 20:35
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.?..作者: 治愈 時間: 2025-3-22 23:10
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.作者: 自愛 時間: 2025-3-23 05:22
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.作者: 混亂生活 時間: 2025-3-23 07:17
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.?..作者: hereditary 時間: 2025-3-23 11:25
Equivalencesor 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.作者: CREEK 時間: 2025-3-23 17:56
Book 2021odeling 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..作者: 認為 時間: 2025-3-23 18:57 作者: 我的巨大 時間: 2025-3-24 01:55 作者: 數(shù)量 時間: 2025-3-24 04:06 作者: 粗野 時間: 2025-3-24 07:42
Lotta Fr?ndberg,Bertil Vilhelmsonough these operators have many properties in common with implications, we show that there is a subtle but important difference. After a profound examination of the main properties of the preference operator, our main goal is the implementation of this operator into neural networks.作者: 確保 時間: 2025-3-24 13:50
Modifiers and Membership Functions in Fuzzy Setslts. Now, we make a suggestion of how modifiers and membership functions can be linked to the logical operators of the system. This unified framework will be useful and aid better interpretability of neural computations in Chap.?..作者: conference 時間: 2025-3-24 18:25 作者: 揉雜 時間: 2025-3-24 19:54
Squashing Functionsle parametrized family of functions that can not only be used for approximating piecewise linear membership functions but also ?ukasiewicz-type logical operators. We show that the derivative of a squashing function is the difference of two sigmoid functions. This fact will come useful in gradient-based applications.作者: botany 時間: 2025-3-25 03:07
Book 2021tools 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 m作者: 冥想后 時間: 2025-3-25 04:24
978-3-030-72282-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: perpetual 時間: 2025-3-25 08:46
Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools978-3-030-72280-7Series ISSN 1434-9922 Series E-ISSN 1860-0808 作者: Vital-Signs 時間: 2025-3-25 15:18 作者: collagen 時間: 2025-3-25 17:14
Studies in Fuzziness and Soft Computinghttp://image.papertrans.cn/e/image/319302.jpg作者: AMBI 時間: 2025-3-25 20:10
https://doi.org/10.1007/978-3-319-67122-2onable to study fuzzy logical systems, where these laws hold. In this chapter, we give a full description of consistent conjunction, disjunction, and negation triples in such systems. We also introduce additional negation operators that naturally define thresholds for better modeling of human thinki作者: Generator 時間: 2025-3-26 03:15 作者: Opponent 時間: 2025-3-26 06:07 作者: 廣大 時間: 2025-3-26 09:46
Aleksandra Terzi?,Dunja Demirovi? Bajramis. Here, we introduce two reasonable approaches for defining these unaries; by repeating the arguments of many-variable operators and by using compositions of negations. This way, hedges and also modalities can be viewed as a part of a logical system. We show that membership functions, which play a 作者: 愛管閑事 時間: 2025-3-26 14:00 作者: Chameleon 時間: 2025-3-26 19:07 作者: 未成熟 時間: 2025-3-26 23:39
https://doi.org/10.1007/978-1-4757-5380-6fferentiability; a property that would be useful for learning systems. In this chapter, we introduce the so-called squashing functions; a differentiable parametrized family of functions that can not only be used for approximating piecewise linear membership functions but also ?ukasiewicz-type logica作者: 朋黨派系 時間: 2025-3-27 01:10
https://doi.org/10.1007/978-3-030-72280-7Computational Intelligence; Neural Networks; Explainable Neural Networks; Fuzzy Logic; Multi-criteria De作者: 強有力 時間: 2025-3-27 09:19
Connectives: Conjunctions, Disjunctions and Negationsonable to study fuzzy logical systems, where these laws hold. In this chapter, we give a full description of consistent conjunction, disjunction, and negation triples in such systems. We also introduce additional negation operators that naturally define thresholds for better modeling of human thinki作者: 不愛防注射 時間: 2025-3-27 11:17 作者: 違法事實 時間: 2025-3-27 14:09 作者: thwart 時間: 2025-3-27 19:13 作者: 的闡明 時間: 2025-3-27 22:53
Aggregative Operatorscus on nilpotent logical systems and describe aggregative operators in such systems ranging from symmetric ones (that treat all the inputs equally) to weighted ones, where some inputs are given more weight than others. As a starting point, instead of associativity, we focus on the necessary and suff作者: 嘲弄 時間: 2025-3-28 02:40 作者: infinite 時間: 2025-3-28 07:18
Squashing Functionsfferentiability; a property that would be useful for learning systems. In this chapter, we introduce the so-called squashing functions; a differentiable parametrized family of functions that can not only be used for approximating piecewise linear membership functions but also ?ukasiewicz-type logica作者: extinct 時間: 2025-3-28 11:22 作者: 真 時間: 2025-3-28 18:27
,Development Process of the Multi-link Torsion Axle (MLTA) – A Space- Optimising Suspension for BEVsgünstigen Ausnahmsf?llen am Leben. Die Mehrzahl der Frühgeborenen mit einem Geburtsgewicht unter 1500 . geht zugrunde. Erst im 8. F?talmonat, bei einem K?rpergewicht von 1800 bis 2000 . und einer L?nge von etwa 40 bis 45 . kann man damit rechnen, da? mehr als die H?lfte der Kinder erhalten werden ka作者: 滴注 時間: 2025-3-28 22:42
Rafael A. Irizarry,Laurent Gautier,Leslie M. Copeutions to this book unveil how teachers, backgrounding the political inherent in all memory practices, often nourish the illusion that the history in which they are engaged is all about addressing the past with a reflexive and disciplined approach..978-3-030-11999-7Series ISSN 2662-7361 Series E-ISSN 2662-737X 作者: nonradioactive 時間: 2025-3-28 23:17 作者: 指數(shù) 時間: 2025-3-29 05:59
and do not go into detailed descriptions of observing techni.This book guides readers (astronomers, physicists, and university students) through central questions of Practical Cosmology, a term used by the late Allan Sandage to denote the modern scientific endeavor to find the cosmological model bes作者: 連鎖 時間: 2025-3-29 11:11
Sample Size, Mean, Standard Deviation, and Standard Error of the Mean,nd the standard error of the mean (s.e.) of these scores. All three of these statistics are basic to the study of statistics and are used frequently within many additional statistical tests. The formulas are presented, explained, and a practical example is given for each formula that shows how the f
