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Bound-based methods for constructing and testing models of dynamical systems

机译:基于边界的动力学系统模型的构建和测试方法

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The main requirement of any procedure for validating models is that it should test whether the assumptions built into the model are justified by the observations and compatible with experience. Statistical model-structure selection formalizes such tests into hypothesis tests, whereas informal validation consists of repeatedly asking whether one believes a feature of the model's behaviour. A procedure intermediate between the two extremes, permitting the use of numerical but nonstatistical background information for testing, has an obvious appeal. Such a procedure is provided by bound-based methods, which represent uncertainty in the model by specifying only that the parameters lie within certain bounds, rather than estimating the parameters and calculating the covariance of the estimates. The parameter bounds are computed from input-output observations, given assumed bounds on the model-output error. They can also embody bounds on any other aspects of the model's behaviour which can be related to its parameter values. The method, and its use in control system design are described.
机译:验证模型的任何程序的主要要求是,它应测试模型中建立的假设是否由观察证明是合理的并且与经验兼容。统计模型结构的选择将这种检验形式化为假设检验,而非正式验证包括反复询问人们是否相信模型行为的特征。介于两个极端之间的过程具有明显的吸引力,该过程允许使用数字但非统计的背景信息进行测试。这种过程由基于边界的方法提供,该方法通过仅指定参数位于特定范围内而不是估计参数并计算估计值的协方差来表示模型中的不确定性。给定模型输出误差的假定范围,根据输入输出观察值计算参数范围。它们还可以体现模型行为的任何其他方面的范围,这些范围可以与模型的参数值相关。描述了该方法及其在控制系统设计中的用途。

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