BP Neural Network and Multiple Linear Regression in Acute Hospitalization Costs in the Comparative Study
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.
Shaobo Zhong, Yimin Cheng and Xilong Qu
J. H. Wu et al., "BP Neural Network and Multiple Linear Regression in Acute Hospitalization Costs in the Comparative Study", Applied Mechanics and Materials, Vols. 50-51, pp. 959-963, 2011