Study on a combined prediction method based on BP neural network and improved Verhulst model

• Main Authors: Tong Niu, Lin Zhang, Shengjun Wei, Baoshan Zhang, Bo Zhang
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2019-12-01
• Series: Systems Science & Control Engineering
• Subjects: Gray Verhulst model; neural network; reverse prediction; defense expenditure prediction
• Online Access: http://dx.doi.org/10.1080/21642583.2019.1650672

The cost prediction is an important part of macroeconomic prediction. The fitting degree of the prediction model not only directly influences the prediction accuracy but also determines the effectiveness of the information provided to the decision maker, especially in the defense economy area. This paper uses the model improving method of reverse prediction and makes the best of the advantage of the Verhulst model of reverse prediction which can solve the problem of ‘small sample, uncertainty’ for the prediction of the defense expenditure. Based on the establishment of a reverse prediction Verhulst model which corrects the initial value, the BP neural network and the combined prediction model based on the BP neural network and improved Verhulst model is further established from the residual sequence dimension with the introduction of the BP neural network, and the analysis validation is conducted through the comparison to the Verhulst-BP model established based on the residual sequence of the traditional Verhulst model. China defense expenditure data collected is tested by practice, showing that this combined prediction model can improve the prediction accuracy greatly.