Method for predicting medicinal material price changing rules based on BP neural network

Abstract

The invention relates to the field of price change law prediction, discloses a method for predicting the price change law of medicinal materials based on BP neural network, and predicts the price change law of medicinal materials. The invention comprises the steps of: taking the price data of medicinal materials at known time as a test sample; creating a BP neural network model; using the data in the test sample as training data to perform network training on the BP neural network; drawing a price trend graph according to the training result, and Judging whether the error between the price trend graph drawn by the training result and the price trend graph drawn by the original data meets the predetermined error, if so, use the BP neural network model after training as the prediction model; output the prediction of future time according to the prediction model obtained in step d Data; draw a price forecast trend chart based on the forecast data of the future time. The invention is suitable for predicting the price change law of medicinal materials.

Description

technical field [0001] The invention relates to the field of price change law prediction, in particular to a method for predicting medicinal material price change law based on BP neural network. Background technique [0002] So far, there are almost no more accurate prediction mathematical model schemes for the development prospects of a certain field. Search CNKI for "Comparison of Population Prediction Methods" (Song Peifeng, Anhui University, 2013), "Research on the Evaluation and Prediction Model of Yellow River Soil Erosion Based on GIS" (Ma Xiao, PLA Information Engineering University, 2008), "Prediction of wind and temperature in high-temperature coal mining face Theory and Numerical Simulation" (2015 Kong Song, China University of Mining and Technology), "Application Research of Gray BP Neural Network Wind Power Prediction" (2013 Ye Aixian, Lanzhou Jiaotong University), "Mathematical Simulation and Design Scheme Optimization of Supercritical Circulating Fluidized Bed...

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Problems solved by technology

Search CNKI for "Comparison of Population Prediction Methods" (Song Peifeng, Anhui University, 2013), "Research on the Evaluation and Prediction Model of Yellow River Soil Erosion Based on GIS" (Ma Xiao, PLA Information Engineering University, 2008), "Prediction of wind and temperature in high-temperature coal mining face Theory and Numerical Simulation" (2015 Kong Song, China University of Mining and Technology), "Application Research of Gray BP Neural Network Wind Power Prediction" (2013 Ye Aixian, Lanzhou Jiaotong University), "Mathematical Simulation and Design Scheme Optimization of Supercritical Circulating Fluidized Bed Boiler" ( Xu Zhi, Zhejiang University, 2012), "Research on BP Neural Network for Market Prediction" (2006, Dai Dan, Wuhan University of Technology), "Application Research of Neural Network in Stock Market Prediction (2007, Shandong Normal University, Hu Jing)", "Based on Neural Network Network Futures Price Forecasting Method Research (2009 Capital Normal University Wang Haijun)" and other forecasting explorations, these research mathematical models can only provide some explorations for the specific issues studied, and achieve corresponding accurate predictions, but it is difficult to widely apply

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