A Scenario Optimization Approach to System Identification with Reliability Guarantees
A Scenario Optimization Approach to System Identification with Reliability Guarantees
复制标题
具有可靠性保证的系统识别场景优化方法
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Julio Deride
中科院分区：
文献类型：
--
作者：
L. G. Crespo;D. Giesy;S. Kenny;Julio Deride
本文提出了一个基于优化的框架，根据多变量，输入输出数据的参数模型的校准。我们专注于连续模型，其输出非线性地（可能是隐式地）依赖于输入和参数。最大似然和场景优化技术相结合，以生成具有相关参数的随机预测模型。此外，预测的可靠性，作为衡量未来的数据落在预测的输出范围之外的概率，形式上使用非凸情景理论有界。通过根据模态分析数据校准具有非共位传感器-致动器对的系统的线性时不变模型来说明该框架。
This paper proposes an optimization-based framework for the calibration of parametric models according to multi-variate, input-output data. We focus on continuous models whose outputs depend nonlinearly (and possibly implicitly) on the inputs and the parameters. Maximum likelihood and scenario optimization techniques are combined to generate stochastic predictor models having dependent parameters. Furthermore, the reliability of the predictor, as measured by the probability of future data falling outside the predicted output ranges, is formally bounded using non-convex scenario theory. This framework is illustrated by calibrating a linear time invariant model of a system having a non-colocated sensor-actuator pair according to modal analysis data.