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Fast recursive basis function algorithms for identification of time-varying processes

机译:用于识别时变过程的快速递归基函数算法

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摘要

When system parameters vary rapidly with time the weighted least squares filters are not capable of following the changes satisfactorily - some more elaborate estimation schemes, based on the method of basis functions, have to be used instead. The basis function estimators have increased tracking capabilities but are computationally very demanding. The paper introduces a new class of adaptive filters, based on the concept of post filtering, which have improved parameter tracking capabilities, typical of the basis function algorithms, but at the same time, have rather low computational requirements, typical of the weighted least squares algorithms.
机译:当系统参数随时间快速变化时,加权最小二乘滤波器无法令人满意地跟踪变化-必须使用一些基于基函数方法的更精细的估计方案。基函数估计器具有增强的跟踪功能,但计算要求很高。本文基于后置滤波的概念,介绍了一类新的自适应滤波器,它具有改进的参数跟踪能力,这是基函数算法的典型,但同时对计算的要求却很低,通常是加权最小二乘算法。

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