Topic
Recursive least squares filter
About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly, and the statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty.
91 citations
Journal Article•
TL;DR: A new algorithm is introduced that attempts to combine the efficiency of filter techniques and the robustness of trust-region methods and is shown to globally converge to zeros of the system or to first-order stationary points of the Euclidean norm of its residual.
Abstract: We introduce a new algorithm for the solution of systems of nonlinear equations and nonlinear least-squares problems that attempts to combine the efficiency of filter techniques and the robustness of trust-region methods. The algorithm is shown, under reasonable assumptions, to globally converge to zeros of the system, or to first-order stationary points of the Euclidean norm of its residual. Preliminary numerical experience is presented that shows substantial gains in efficiency over the traditional monotone trust-region approach.
91 citations
TL;DR: The binormalized data-reusing least mean squares (BNDR-LMS) algorithm is analyzed, which corresponds to the affine projection algorithm for the case of two projections, and compares favorably with other normalized LMS-like algorithms when the input signal is correlated.
Abstract: Normalized least mean squares algorithms for FIR adaptive filtering with or without the reuse of past information are known to converge often faster than the conventional least mean squares (LMS) algorithm. This correspondence analyzes an LMS-like algorithm: the binormalized data-reusing least mean squares (BNDR-LMS) algorithm. This algorithm, which corresponds to the affine projection algorithm for the case of two projections, compares favorably with other normalized LMS-like algorithms when the input signal is correlated. Convergence analyses in the mean and in the mean-squared are presented, and a closed-form formula for the mean squared error is provided for white input signals as well as its extension to the case of a colored input signal. A simple model for the input-signal vector that imparts simplicity and tractability to the analysis of second-order statistics is fully described. The methodology is readily applicable to other adaptation algorithms of difficult analysis. Simulation results validate the analysis and ensuing assumptions.
91 citations
TL;DR: An auxiliary model based recursive least squares algorithm is developed for identifying the parameters of the proposed system by means of the auxiliary model identification idea and the simulation results confirm the conclusion.
91 citations
TL;DR: It is demonstrated how the construction of an algorithm for a particular problem that falls in one of the classes of optimization problems under study, reduces to a simple combination of tools.
90 citations