<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to be efficient and accurate monitoring and early warning hospital financial software capabilities of hospitals, the author presents the working model of the different operations of the solutions should be used for financial forecasting in hospitals, in order to provide useful information to decision makers. Those. By the equality of the measure of suspension of the function of the difference between the difference and the difference between the difference in some cases, and by the use of the limits of the Solving the problem of the difference in the difference between the difference, the limit of the solution of the type of delay measure the difference. yes, and then the deep belief network is studied with the data set taken to measure to get the effects of the network; Finally, the deep belief network model is used for financial forecasting, which is compared with other methods in machine learning. The results show that with the increase of the number of network layers, the recognition accuracy is also improved. However, the number of network layers is not optimal. When the number of network layers is 3, the recognition performance is the best, which shows that the number of hidden layers in the deep trust network needs special analysis in combination with specific applications and practical information, in order to obtain the best number of network layers. It is clear that the deep belief network model has the best data fitting performance in terms of root mean square error and goodness of fit.</p>
</abstract>

In order to be efficient and accurate monitoring and early warning hospital financial software capabilities of hospitals, the author presents the working model of the different operations of the solutions should be used for financial forecasting in hospitals, in order to provide useful information to decision makers. Those. By the equality of the measure of suspension of the function of the difference between the difference and the difference between the difference in some cases, and by the use of the limits of the Solving the problem of the difference in the difference between the difference, the limit of the solution of the type of delay measure the difference. yes, and then the deep belief network is studied with the data set taken to measure to get the effects of the network; Finally, the deep belief network model is used for financial forecasting, which is compared with other methods in machine learning. The results show that with the increase of the number of network layers, the recognition accuracy is also improved. However, the number of network layers is not optimal. When the number of network layers is 3, the recognition performance is the best, which shows that the number of hidden layers in the deep trust network needs special analysis in combination with specific applications and practical information, in order to obtain the best number of network layers. It is clear that the deep belief network model has the best data fitting performance in terms of root mean square error and goodness of fit.

The boundedness of functional differential equations of hospital financial software under information

Affiliated Hospital of Hebei University of Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to be efficient and accurate monitoring and early warning hospital financial software capabilities of hospitals, the author presents the working model...

The boundedness of functional differential equations of hospital financial software under information

Data publikacji: 28 kwi 2023

Zakres stron: 1591 - 1600

Otrzymano: 07 kwi 2022

Przyjęty: 18 wrz 2022

DOI: https://doi.org/10.2478/amns.2023.1.00116

Słowa kluczowe
Informatization, Hospital financial software, Functional differential, Mathematical equation solution, Boundedness

© 2023 Jingguang Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The boundedness of functional differential equations of hospital financial software under information

Data publikacji: 28 kwi 2023

Zakres stron: 1591 - 1600

Otrzymano: 07 kwi 2022

Przyjęty: 18 wrz 2022

DOI: https://doi.org/10.2478/amns.2023.1.00116

Słowa kluczoweInformatization, Hospital financial software, Functional differential, Mathematical equation solution, Boundedness

© 2023 Jingguang Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Informatization, Hospital financial software, Functional differential, Mathematical equation solution, Boundedness