AWNG-BP prediction technique study based on nonlinear combination – A case study of prediction of food supply chain in rural areas of Hubei Province, China

The demands to food in China rural areas are strong while construction of food supply chain lags. Lack of statistical data presents difficulties in construction of food supply chain in China rural areas. This paper designed the prediction technique based on nonlinear combined AWNG-BP. Firstly, this prediction technique used BP neural network to nonlinearly combine conventional regression prediction, three exponential smoothing prediction and grey sequence prediction, in order to reduce negative effects on prediction results caused by data missing and abnormal data. Secondly, modified the inherent defect and especially solve structure optimization problems of general BP neural network, by modifying error function and introducing dynamic self-adapting and genetic algorithm of ecological niche. Thirdly, made sensitivity analysis to three input variables of the prediction technique based on nonlinear combined AWNG-BP, then turned the sensitivity analysis data into a sensitivity curve and synthesized the three sensitivity curves into an image. The image prediction results indicate: first, each single prediction technique has imbalanced effects on prediction results of food supply chain scale in rural areas of Hubei Province of China and the multiple regression and three exponential smoothing prediction affect system evidently; second, total consumption of cold chain food in Hubei Province has been in saturation and will decline slightly in future.