中国农业科学2012，Vol.45Issue(23)：4924-4930,7.DOI:10.3864/j.issn.0578-1752.2012.23.020
基于遗传算法优化的BP神经网络的组合预测模型方法研究
Research on the Combined Forecast Model Method Based on BP Neural Network Improved by Genetic Algorithm
摘要
Abstract
[Objective] The combined forecasting model for studying the classic swine fever morbidity was proposed. [Method] The data was processed by ARIMA and GM(1,1) initially, then the results were used as the inputs of the tnajorizing BP neural network. [Result] The combined model was used to analyze the monthly data from 2000/01 to 2008/05, and the accuracy of the forecasting results from 2008/06 to 2009/06 was 97.379%. The prediction accuracy of the combined model increased by 5.469%, 3.499%, and 1.188%, respectively, compared with BP neural network, ARIMA, GM(1,1), which suggest that the combined model is more steady than traditional methods. [Conclusion] This research has supplied an efficient analytical tool for animals diseases forecasting work, verified the feasibility of the combined model in animal diseases forecasting research, and also has provided references to other animal diseases.关键词
组合模型/ARIMA/GM(1,1)/遗传算法/BP神经网络Key words
combined model/ARIMA/GM (1, 1)/genetic algorithm/BP neural network引用本文复制引用
梁毅,刘世洪..基于遗传算法优化的BP神经网络的组合预测模型方法研究[J].中国农业科学,2012,45(23):4924-4930,7.基金项目
国家"863"专题"商品猪精细养殖网络系统研发"项目(2003AA209050-6) （2003AA209050-6）
