共被期刊论文引用了2次
您的检索式：您选中1篇文献正在查看引证文献汇总
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | 局域法邻近点选取对降雨量预测精度影响研究显示文摘基于混沌理论的局域法对非线性、非平稳的降雨系统的预测较为适用,而邻近点个数与混沌局域法预测精度密切相关,但在运用该模型对降雨量预测时却鲜有人考虑邻近点个数问题,邻近点个数选择过少可能将历史运动趋势忽略,选取过多将加大计算量,甚至引入伪邻近点,导致预测精度降低。鉴于此,研究了BIC信息准则用于混沌局域法的邻近点个数的确定,并采用实测月降雨量数据验证所提出方法。结果表明,运用BIC信息准则优选邻近点能显著提高月降雨量预测精度,预测的平均绝对误差由3.640%降低到2.511%。 | 刘年东 杜坤 周明 李诚 胡琪勇 | 2016 | 给水排水2016，42，S1： | 3 |
| 2 | A Novel Hybrid FA-Based LSSVR Learning Paradigm for Hydropower Consumption Forecasting显示文摘Due to the nonlinearity and nonstationary of hydropower market data, a novel hybrid learning paradigm is proposed to predict hydropower consumption, by incorporating firefly algorithm(FA) into least square support vector regression(LSSVR), i.e., FA-based LSSVR model. In the novel model, the powerful and effective artificial intelligence(AI) technique, i.e., LSSVR, is employed to forecast hydropower consumption. Furthermore, a promising AI optimization tool, i.e., FA, is especially introduced to address the crucial but difficult task of parameters determination in LSSVR(e.g.,hyper and kernel function parameters). With the Chinese hydropower consumption as sample data,the empirical study has statistically confirmed the superiority of the novel FA-based LSSVR model to other benchmark models(including existing popular traditional econometric models, AI models and similar hybrid LSSVRs with other popular parameter searching tools), in terms of level and directional accuracy. The empirical results also imply that the hybrid FA-based LSSVR learning paradigm with powerful forecasting tool and parameters optimization method can be employed as an effective forecasting tool for not only hydropower consumption but also other complex data. | TANG Ling WANG Zishu LI Xinxie YU Lean ZHANG Guoxing | 2015 | Journal of Systems Engineering and Electronics2015，26，5： | 3 |
您所在的机构暂未开通此部分的功能，请联系管理员