Asymptotically optimal smoothing of averaged LMS estimates for regression parameter tracking
Copyright policy )Asymptotically optimal smoothing of averaged LMS estimates for regression parameter tracking
This paper considers the estimates formed by the LMS (least mean-squares) algorithm for a standard linear regression. It is shown that smoothing the LMS estimates using a matrix updating will lead to smoothed estimates with optimum tracking properties, also in case the true parameters are slowly changing as a random walk. It is also shown that the same accuracy can be obtained for a modified algorithm, SLAMS, which is based on averages and requires far fewer computations.
- Linköping University Sweden
Least squares and related methods for stochastic control systems, asymptotic MSE, linear regression, Data smoothing in stochastic control theory, smoothing, slow random walk, LMS, parameter tracking
Least squares and related methods for stochastic control systems, asymptotic MSE, linear regression, Data smoothing in stochastic control theory, smoothing, slow random walk, LMS, parameter tracking
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