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• W3025031612 startingPage "92861" @default.
• W3025031612 abstract "In this paper, we extend the original Normalized Least Mean Fourth (NLMF) and Normalized Least Mean Square (NLMS) adaptive filtering algorithms into Geometric Algebra (GA) space to enable them to process multidimensional signals. We redefine the cost functions and propose the GA based NLMF and NLMS algorithms (GA-NLMF & GA-NLMS). We take full advantage of the ability of GA to represent multidimensional signals in GA space. GA-NLMS minimizes the cost function of the normalized mean square of the error signal, and remain stable as the input signal of the filter increases. GA-NLMS has fast convergence rate but higher steady-state error. The GA-NLMF algorithm minimizes the cost function of the normalized mean fourth of the error signal. Simulation results show that our proposed GA-NLMS adaptive filtering algorithm outperforms original NLMS algorithm in terms of convergence rate and steady-state error, and GA-NLMF outperforms both NLMF and GA-NLMS algorithms. GA-NLMF has faster convergence rate and lower steady state error, which is proved in the experiments." @default.
• W3025031612 created "2020-05-21" @default.
• W3025031612 creator A5012772285 @default.
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• W3025031612 date "2020-01-01" @default.
• W3025031612 modified "2023-09-30" @default.
• W3025031612 title "A Normalized Adaptive Filtering Algorithm Based on Geometric Algebra" @default.
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• W3025031612 doi "https://doi.org/10.1109/access.2020.2994230" @default.
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