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基于全局敏感度分析的GreenLab模型参数估计研究
Alternative TitleStudy on Fitting Parameters of GreenLab Model Based on Global Sensitivity Analysis
林玉彬
Subtype工程硕士
Thesis Advisor胡包钢
2012-05-25
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机技术
Keyword功能结构模型 Greenlab 源库比 敏感度分析 Functional-structural Model Greenlab Source-sink Ratio Sensitivity Analysis
Abstract利用数学与计算机技术对植物生长发育过程进行建模、模拟及可视化,有助于探索植物生长过程的规律,预测植物最终产量,对于指导农业实践具有重要意义。GreenLab模型作为植物生长功能结构模型的代表之一,已逐步应用到各种作物及树木的生长模拟中。通常来说,在应用模型之前需要对其进行标定校准。GreenLab模型中的隐含参数不能通过测量得到,需要通过拟合测量数据进行参数反求。传统的参数反求过程采用基于最小二乘拟合的方法使模型输出逼近田间实测数据。然而,由于基于梯度下降的方法容易收敛到局部极小值,且依赖于拟合所选取的参数初值,在实践中往往未能达到满意的拟合效果。基于此,本文对GreenLab模型的参数敏感度进行了研究,主要工作和创新点包括: (1)利用GreenLab模型的优势,将多目标输出模型的敏感度分析转化为对每个周期模型源库比的分析,能从全局上反映参数对于模型系统的影响; (2)利用敏感度分析的结果,将模型参数分阶段拟合,实现模型参数反求过程自动化,减少对于拟合参数初值的依赖。
Other AbstractThe use of mathematics and computer technology to model, simulate and visualize the process of plant growth modeling could help us to explore the law of plant growth and predict the final yield of plants, which is of great significance for guiding agricultural practices. As one of the representatives of functional-structural models of plant growth, GreenLab has been gradually applied to various crops and tree growth simulation. In general, calibration is needed before models can be applied. Hidden parameters in GreenLab model, which cannot be measured directly, should be fitted using measurement by model inversion. Traditional fitting process utilizes the least squared method to approximate the model output to field data. However, fitting methods based on gradient descent frequently converge to local minimum and rely on the initial values of parameters chosen for iteration, which often lead to poor fitting result. In summary, sensitivity analysis of the GreenLab model was studied in this paper and the accomplishments of this work include the following aspects: (1) Sensitivity analysis of model with multiple outputs was carried out by analyzing the source-sink ratio at each growth cycle without missing the global information, which reflects the advantages of the GreenLab model. (2) Model parameters were fitted automatically and sequentially by choosing appropriate subset based on the sensitivity analysis result. Experiment showed that the fitting process is less dependent on prior knowledge of possible parameter values.
shelfnumXWLW1806
Other Identifier2009M8014629009
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7624
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
林玉彬. 基于全局敏感度分析的GreenLab模型参数估计研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2012.
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