Strongly consistent recursive regression estimation under depended observations

K. Chernyshov
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Abstract

The paper is focused on establishing strong consistency of recursive estimates of nonlinear characteristics of dynamic systems. To describe the shape of the nonlinearities, the regression function kernel type estimates are used. Within the approach presented, a feature of the technique is considering a case of mutually dependent observations. Simultaneously, only mild and easy verified assumptions with respect to the system's input and output processes, as well as to the external disturbances, are involved.
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依赖观测下的强一致递归回归估计
本文主要研究动态系统非线性特性的递推估计的强相合性。为了描述非线性的形状,使用回归函数核类型估计。在提出的方法中,该技术的一个特点是考虑相互依赖观察的情况。同时,对于系统的输入和输出过程,以及外部干扰,只涉及温和且易于验证的假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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