论文标题
线性参数化系统的间隔值递归估计框架
An interval-valued recursive estimation framework for linearly parameterized systems
论文作者
论文摘要
本文提出了一个递归间隔值估算框架，用于识别线性参数化系统的参数，该系统可能会缓慢延时。假定模型误差（可能由测量噪声或模型不匹配或两者组成）是未知的，但在每个时间瞬间都在已知的时间间隔内。在这种情况下，提出的方法依赖于给定参考点值递归估计器产生的误差，例如，众所周知的递归最小二乘算法。我们讨论了计算复杂性与估计参数间隔的紧密度之间的权衡。
This paper proposes a recursive interval-valued estimation framework for identifying the parameters of linearly parameterized systems which may be slowly time-varying. It is assumed that the model error (which may consist in measurement noise or model mismatch or both) is unknown but lies at each time instant in a known interval. In this context, the proposed method relies on bounding the error generated by a given reference point-valued recursive estimator, for example, the well-known recursive least squares algorithm. We discuss the trade-off between computational complexity and tightness of the estimated parametric interval.
