Convolution of Decomposition Integrals

Šeliga, Adam

doi:10.3390/math10050747

Cited by 2 publications

(1 citation statement)

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“…Name established a financial supply chain risk model based on convolutional neural network for the supply chain of small and medium-sized enterprises, which solved the complex problems of supply chain financial decision-making and supply chain enterprise supervision and review, and improved the feature recognition of supply chain platforms to provide better advice and consultation for financial institutions and supply chain platforms. Šeliga examined 4 different types of aggregate function convolutions (the upper, the lower, the super-, and the sub-convolution) in the setting of sub-and super-decomposition integrals defined over finite spaces [12]. The super-additive transformation of the sub-decomposition integral and the sub-additive transformation of the super-decomposition integral are fully described, and the specific decomposition integral and its generalization are successfully applied.…”

Section: Applications Of Fourier Transformmentioning

confidence: 99%

A survey of convolution

Zou

2023

Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022)

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Convolution is a mathematical method of integral transformation and an important operation in analytical mathematics. The concept of convolution can also be extended to sequence, measure and generalized functions. Convolution has many applications in mathematics and engineering. For example, it can be widely used as a linear operation in image filtering, digital signal processing, electronic engineering and other application scenarios such as fluid mechanics, statistics, physics, astronomy, thermodynamics, optics. It plays a very key role in such important disciplines as it is a very common tool to help people solve problems. This paper gives a more detailed description of continuous convolution, discrete convolution and two-dimensional convolution through formula method and definition method, and points out their common application fields, and then discusses discrete convolution and continuous convolution through common scenes in life which is the simple explanation. The deep application of convolution in the signal field and Fourier transform field is sorted out. Finally, combining with the background of the era of big data, the multi-faceted applications of the future convolutional network neural field are discussed.

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Section: Applications Of Fourier Transformmentioning

confidence: 99%