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.
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TL;DR: This paper gives a slightly more efficient and slightly more general version of this variable projection algorithm, designed to take advantage of the structure of a problem whose variables separate in this way.
Abstract: Nonlinear least squares problems frequently arise for which the variables to be solved for can be separated into a linear and a nonlinear part. A variable projection algorithm has been developed recently which is designed to take advantage of the structure of a problem whose variables separate in this way. This paper gives a slightly more efficient and slightly more general version of this algorithm than has appeared earlier.
38 citations
TL;DR: A new diffusion least mean squares algorithm that utilizes adaptive gains in the adaptation stage for the sparse distributed estimation problem is proposed and the mean stability analysis is provided to establish sufficient condition for the algorithm to converge in the mean sense.
Abstract: We propose a new diffusion least mean squares algorithm that utilizes adaptive gains in the adaptation stage for the sparse distributed estimation problem. We derive the optimal gains that attain a minimum mean-square deviation and propose an adaptive gain control method. We provide the mean stability analysis to establish sufficient condition for the algorithm to converge in the mean sense. The algorithm achieves higher convergence speed than the sparsity-constrained algorithms, regardless of the sparsity of the vector of interest.
38 citations
TL;DR: Comparison of results amongst recently proposed Artificial Bee Colony Least Square (ABC–LS), Bacteria Foraging Optimized Recursive Le least Square (BFO–RLS) and FA-RLS algorithms reveals that proposed FA– RLS algorithm is the best in terms of accuracy, convergence and computational time.
38 citations
Journal Article•
TL;DR: This paper presents performance comparison of three estimation techniques used for peak load forecasting in power systems, genetic algorithms (GA), least error squares (LS) and, least absolute value filtering (LAVF).
Abstract: This paper presents performance comparison of three estimation techniques used for peak load forecasting in power systems. The three optimum estimation techniques are, genetic algorithms (GA), least error squares (LS) and, least absolute value filtering (LAVF). The problem is formulated as an estimation problem. Different forecasting models are considered. Actual recorded data is used to perform the study. The performance of the above three optimal estimation techniques is examined. Advantages of each algorithms are reported and discussed. Keywords—Forecasting, Least error squares, Least absolute Value, Genetic algorithms.
37 citations
TL;DR: The comparison between the state-space-based channel estimation algorithm and the FIR-based Recursive Least Squares algorithm shows the former is a more robust modeling approach than the latter.
37 citations