A new fuzzy unit selection cost function optimized by relaxed gradient descent algorithm

Rojc, Matej; Mlakar, Izidor

doi:10.1016/j.eswa.2020.113552

Cited by 8 publications

(1 citation statement)

References 59 publications

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“…The cost function formula [34,35] is optimized by gradient descent algorithm, and the weight is updated with iteration to complete the fine adjustment of evaluation model parameters. Using the construction model, the enterprise tax risk is evaluated through the construction of index system, and the distribution of enterprise tax risk constraint indicators is shown in Table 1.…”

Section: Construction Of Enterprise Tax Risk Intelligentmentioning

confidence: 99%

Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning

Guo¹

2022

Wireless Communications and Mobile Computing

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Enterprise tax risk involves many aspects. In order to accurately realize enterprise tax risk assessment, an intelligent assessment method of enterprise tax risk based on deep learning is proposed. Collect enterprise tax risk information; reasonably distinguish the same type of risk information according to category, type, and appearance; extract the correlation characteristics of enterprise tax risk assessment; use the correlation characteristics to build a deep learning enterprise tax risk assessment model; search the best parameters of the automatic encoder; build a stacked automatic encoder based on the automatic encoder; combine it with the classifier; and complete the design of enterprise tax risk intelligent evaluation model. The experimental results show that the deviation ratio of Receiver Operating Characteristic (ROC), the risk assessment index of the proposed method, is less than 0.38 as a whole. Therefore, the enterprise tax risk intelligent assessment method based on deep learning is better.

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Section: Construction Of Enterprise Tax Risk Intelligentmentioning

confidence: 99%