BP Neural Network and Multiple Linear Regression in Acute Hospitalization Costs in the Comparative Study

Jian Hui Wu; Guo Li Wang; Jing Wang; Yu Su

doi:10.4028/www.scientific.net/AMM.50-51.959

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Investigation and Analysis about the Scientific Research Ability of Clinical Teachers Guiding Nursing Undergraduates
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Analysis to Postgraduate Employment Plight under Dual-Economy
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BP Neural Network and Multiple Linear Regression in Acute Hospitalization Costs in the Comparative Study
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Application of the BP Nerve Network Model in the Analysis of Factors Affecting Hospitalization Expenses
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 50-51BP Neural Network and Multiple Linear Regression...

BP Neural Network and Multiple Linear Regression in Acute Hospitalization Costs in the Comparative Study

Abstract:

The BP neural network is the important component of artificial neural networks, and gradually becomes a branch of the computation statistics. With its many characteristics such as large-scale parallel information processing, excellent self-adaptation and self-learning, the BP neural network has been used in solving the complex nonlinear dynamic system prediction. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. By means of contrasting the BP neural network and the multi-dimensional linear regression ,this article discoveries that the BP neural network fitting ability is more stronger, the prediction performance is more stable, may be further applied and promoted in analysis and forecast of the continual material factor.

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Periodical:

Applied Mechanics and Materials (Volumes 50-51)

Pages:

959-963

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.50-51.959

Citation:

Cite this paper

Online since:

February 2011

Authors:

Jian Hui Wu, Guo Li Wang, Jing Wang, Yu Su

Keywords:

BP Neural Network (BPNN), LM Algorithm, Multiple Linear Regression, Prediction

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