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植物生长模型的实用识别:一个统一的框架及其对三个局部指数的规范

Jean Velluet¹, Antonin Della Noce¹, Véronique Letort¹

¹MICS Laboratory, CentraleSupelec, Paris-Saclay University, Gif-sur-Yvette, France.

Plant phenomics (Washington, D.C.)

|February 13, 2024

概括

这项研究统一了机械植物生长模型的识别性分析. 它引入了一个新的风险指数,用于实际识别,提高参数估计的准确性和生物解释.

科学领域:

在植物科学中的数学建模.
系统生物学和计算建模.
参数估计和可识别性分析

背景情况:

机械植物生长模型对于理解系统动态至关重要,但依赖于准确的参数估计.
可识别性分析确保估计参数反映真实生物价值,解决模型独特性方面的挑战.
对于结构性可识别性的现有定义是一致的,但实际的可识别性缺乏共识和标准化的量化.

研究的目的:

为研究模型识别提供统一的框架,可适应各种特定应用定义.
评估和比较用于量化实际可识别性的常见方法,突出其局限性.
提出一种新的风险指数,以基于概率概率的置信区间进行实际识别.

主要方法:

为可识别性分析制定统一的理论框架.
对对线性指数,概率概率和实际识别的平均相对误差进行比较分析.
引入一个新的风险指数,该指数来自基于概率的信任区间.
使用离散时间 (LNAS) 和连续时间植物生长模型的应用和验证.

主要成果:

展示现有实际可识别量化方法的本地版本的局限性.
建议并验证一个新的,更强大的风险指数,以实现实际识别.
在各种植物生长建模场景中成功说明了可识别性概念.

结论:

可识别性分析对于补充机械模型中的参数估计至关重要.
拟议的统一框架和新的风险指数为建模人员提供了改进的指导.
这项工作提高了植物生长模型的可靠性和生物解释性.

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