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Analyzing the Influence of Measurements in Dynamical Parameter Identification Using Parametric Sensitivities

机译:使用参数敏感度分析测量测量的影响

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In many modern applications, a central task is to model a dynamical behavior with ordinary differential equations. A common way to identify parameters within such a model is to fit its output against given measurements. Since it can be difficult to understand the connection between measurements and the parameter identification result, it is desirable to develop methods for analyzing this aspect.In this paper, we show how information from parametric sensitivity analysis can be used to gain a better insight into the impact of certain measurement regions on the identified model parameters. We parameterize the measurements by a B-spline regression and formulate the task of parameter identification using a collocation approach. In the resulting nonlinear optimization problem, we consider the B-spline coefficients as perturbation parameters. Next, we identify the model parameters and compute their parametric sensitivities with respect to these perturbations. In a final step, we evaluate a newly developed measure to characterize the desired influence. The corresponding optimization problems are solved with the nonlinear programming solver WORHP in combination with its integrated module WORHP Zen, which computes the required parametric sensitivities efficiently. We demonstrate the proposed approach by applying it to the example of parameter identification of a driving car.
机译:在许多现代应用中,一个中央任务是利用普通微分方程模拟动态行为。在这种模型中识别参数的常用方法是将其输出符合给定测量。由于可能难以理解测量和参数识别结果之间的连接,因此希望开发用于分析这一方​​面的方法。在本文中,我们展示了参数敏感性分析中的信息如何用于获得更好的洞察力某些测量区域对鉴定的模型参数的影响。我们通过B样条回归参数化测量,并使用搭配方法制定参数识别的任务。在由此产生的非线性优化问题中,我们认为B样条系数作为扰动参数。接下来,我们确定模型参数并在这些扰动方面计算它们的参数敏感性。在最后一步中,我们评估新开发的措施以表征所需的影响。相应的优化问题与非线性编程求解器WorHP与其集成模块的组合组合的Worhp Zen解决,其有效地计算所需的参数敏感性。我们通过将所提出的方法施加到驾驶汽车的参数识别示例来证明所提出的方法。

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