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Multiobjective parameter estimation for non-linear systems: affine information and least-squares formulation

机译:非线性系统的多目标参数估计:仿射信息和最小二乘公式

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This paper defines a class of system information - affine information - that includes both the dynamic residuals and some types of auxiliary information that can be used in system parameter estimation as special cases. The types of information that can be cast under the a. ne information format give rise to quadratic functions that measure the extent to which a model fits such information, and that can be aggregated in a single weighted quadratic cost functional. This allows the definition of a multiobjective methodology for parameter estimation in non-linear system identification, which allows taking into account any type of a. ne information. The results are presented in terms of a set of efficient solutions of the multiobjective estimation problem - such a solution set is more meaningful than a single model. Since any a. ne information leads to a convex (quadratic) functional, the whole set of efficient solutions is exactly accessible via the minimization of the quadratic functional with different weightings, via a least-squares minimization (a non-iterative, computationally inexpensive procedure). The decision stage, in which a single model is chosen from the Pareto-set, becomes well-defined with a single global solution. Residual variance fixed point location, static function and static gain are shown to fit in the class of affine information. A buck DC-DC converter is used as example.
机译:本文定义了一类系统信息-仿射信息-包括动态残差和某些类型的辅助信息,这些信息可以在特殊情况下用于系统参数估计。可以在a下强制转换的信息类型。信息格式会产生二次函数,这些二次函数可度量模型拟合此类信息的程度,并且可以汇总为单个加权二次成本函数。这允许定义用于非线性系统识别中的参数估计的多目标方法,从而可以考虑任何类型的a。新信息。根据多目标估计问题的一组有效解决方案来表示结果-这种解决方案集比单个模型更有意义。由于任何一个。如果信息导致凸(二次)函数,则可以通过最小化最小二乘方最小化(一种非迭代的,计算上不昂贵的过程),通过最小化具有不同权重的二次函数来完全访问整个有效解。从帕累托集合中选择单个模型的决策阶段,通过单个全局解决方案就可以很好地定义。剩余方差不动点位置,静态函数和静态增益显示为适合仿射信息类别。以降压DC-DC转换器为例。

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