| 基于进化神经网络的参考作物腾发量预测 |
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| 引用本文： | 崔远来,马承新,沈细中,马吉刚.基于进化神经网络的参考作物腾发量预测[J].水科学进展,2005,16(1):76-81. |
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| 作者姓名： | 崔远来 马承新 沈细中 马吉刚 |
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| 作者单位： | 1.武汉大学水资源与水电工程科学国家重点实验室, 湖北, 武汉, 430072; |
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| 基金项目： | 山东省科技发展计划项目(鲁科022150118)~~ |
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| 摘 要： | 利用遗传算法的全局空间寻优功能和BP网络映射能力强的优点,建立了以遗传算法确定最优网络结构的进化神经网络(GA-ANN)模型,用来预测参考作物腾发量(ET0).设计多组数字实验处理,研究了输入因子间相关性对模型预测准确性的影响,并验证了最优网络模型结构,即预测ET0的理想GA-ANN模型中以日平均气温、日照时数及日序数为输入因子.实例分析表明,该模型克服了BP网络输入层、隐含层节点确定的盲目性,适应性强,精度高,可用于ET0预测.
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| 关 键 词： | 参考作物腾发量 神经网络 遗传算法 预测 |
| 文章编号： | 1001-6791(2005)01-0076-06 |
| 收稿时间： | 2003-10-29 |
| 修稿时间： | 2003年10月29 |
Predicting reference evaportranspiration based on artificial neural network with genic arithmetic |
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| CUI Yuan-lai.Predicting reference evaportranspiration based on artificial neural network with genic arithmetic[J].Advances in Water Science,2005,16(1):76-81. |
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| Authors: | CUI Yuan-lai |
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| Institution: | 1.State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China;2.Shandong Province Water Resources Bureau, Ji'nan 250013, China |
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| Abstract: | A model of Artificial Neural Network with Genic Arithmetic(GA-ANN) is established to predict reference evapotranspiration.This model integrates the merits of seeking for a global optimum solution by using genic arithmetic and the well-mapping capacity of the back propagation neural network.It can determinate the optimum model structure automatically.Eight groups of model input factors' composition are set up,and their correlative influence on the model's forecasting precision are studied.An optimum model structure for predicting short time period(daily and decade) reference evapotranspiration is present,in which only daily mean temperature,sunshine hours and the Julian day's ordinal number are considered as the input factors.A case study shows that the model overcomes the disadvantages in determinating the model structure when using back propagation neural network.And it has high precision with good adaptability and feasibility. |
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| Keywords: | reference evaportranspiration artificial neural network genic arithmetic predicting |
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