Aug 25, 2020 · This brief analyzes the steady-state mean-square error (MSE) performance of the tensor LMS algorithm, which can provide performance prediction and design ...
This brief analyzes the steady- state mean-square error (MSE) performance of the tensor LMS algorithm, which can provide performance prediction and design.
This brief analyzes the steady-state mean-square error (MSE) performance of the tensor LMS algorithm, which can provide performance prediction and design ...
This paper presents a novel adaptive algorithm based on RZA-LMS for sparse signal and system identification. The RZA-LMS algorithm generates a zero attractor in ...
It is shown that the tracking Mean. Square Error (MSE) results from the tradeoff between the gradient part which is µ-increasing and the lag contribution which ...
Missing: Tensor | Show results with:Tensor
In this paper, we present a steady-state MSE and tracking performance analysis of the QOBE algorithm. The classic approach is to analyze the transient behavior ...
Missing: Tensor | Show results with:Tensor
The presented theoretical formula can help designers predict the steady-state performance of the CLMS algorithm and tune its step-size to attain a desired ...
Missing: Tensor | Show results with:Tensor
People also ask
What is the LMS algorithm?
What is least mean squares LMS adaptive filtering?
Steady-State Mean-Square Error Performance Analysis of the Tensor LMS Algorithm ... This brief analyzes the steady-state mean-square error (MSE) performance ...
Mean-square error (MSE) performance analysis is conducted for a novel distributed least-mean square (D-LMS) algorithm, which is based on consensus, in-network, ...
Nov 30, 2023 · In this paper we look at the steady-state cancellation error considering not only the MSE, but also the variance of the squared-error curve.
Missing: Tensor | Show results with:Tensor