ArticleCircuits, systems, and signal processing2022
Identification of the ARX Model with Random Impulse Noise Based on Forgetting Factor Multi-error Information Entropy.
Article in Circuits, systems, and signal processing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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1 author.
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Abstract
Entropy has been widely applied in system identification in the last decade. In this paper, a novel stochastic gradient algorithm based on minimum Shannon entropy is proposed. Though needing less computation than the mean square error algorithm, the traditional stochastic gradient algorithm converges relatively slowly. To make the convergence faster, a multi-error method and a forgetting factor are integrated into the algorithm. The scalar error is replaced by a vector error with stacked errors. Further, a simple step size method is proposed and a forgetting factor is adopted to adjust the step size. The proposed algorithm is utilized to estimate the parameters of an ARX model with random impulse noise. Several numerical solutions and case study indicate that the proposed algorithm can obtain more accurate estimates than the traditional gradient algorithm and has a faster convergence speed.
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