| 引用本文: | 雷鸣雳,冯祖仁.参数由递推辨识的新型GM(1, 2)预测模型[J].控制理论与应用,2013,30(2):233~237.[点击复制] |
| LEI Ming-li,FENG Zu-ren.A novel GM(1, 2) forecasting model with parameters identified recursively[J].Control Theory and Technology,2013,30(2):233~237.[点击复制] |
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| 参数由递推辨识的新型GM(1, 2)预测模型 |
| A novel GM(1, 2) forecasting model with parameters identified recursively |
| 摘要点击 2374 全文点击 1059 投稿时间：2012-01-17 修订日期：2012-08-30 |
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| DOI编号 10.7641/CTA.2013.20055 |
| 2013,30(2):233-237 |
| 中文关键词 灰色预测 GM(1, 2)模型 参数辨识 粒子群算法 |
| 英文关键词 grey prediction GM(1,2) model parameter identification particle swarm optimization (PSO) |
| 基金项目 国家自然科学基金资助项目(60875043); 博士点基金资助项目(20100201110031). |
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| 中文摘要 |
| 为改善模型预测性能, 提出一种GM(1, 2)预测新模型. 根据模型定义式直接推导获得模型预测值递推表达式, 应用粒子群算法对递推表达式参数进行辨识. 典型算例表明, 新模型收敛速度快, 较普通及文献中改进GM(1, 2)模型具有更高的预测精度. |
| 英文摘要 |
| To improve the prediction performance, we propose a novel GM(1,2) model for prediction. The recursive prediction equations are derived directly from the definition of the model. The parameters of prediction equations are identified by using the particle swarm optimization algorithm (PSO). Typical numerical examples are given to demonstrate that the novel GM(1,2) model provides faster convergence rate and higher prediction precision than conventional GM(1,2) models and other improved GM(1,2) models mentioned in references. |
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