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variants - Leaky LMS Algorithm Leaky Convergence of...

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Leaky LMS Algorithm Convergence of tap-weight error modes dependent on conditioning of input : χ (R). For ill-conditioned inputs tap-weight error modes are undriven and undamped. Stability and convergence time issues of concern for ill- conditioned inputs. Leaky LMS Algorithm Cost function: Tap-weight update: Tap-weight converges in mean to a biased solution: Tap-weight error modes stabilized if : Sign Sign -- -- based Algorithms Sign-error algorithm: Sign-data algorithm: Sign-sign algorithm: Sign - based Algorithms Incorporate 2-bit quantization of estimation error e[n] and/or tap-input u[n]. Tap-weight update not guided by gradient. Tap-weights do not converge to optimal Wiener solution. May need regularization to stabilize the tap-weight update.
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Affine Affine Projection Algorithm Tap-weight adjustment of NLMS: w[n+1] – w[n] in the direction of u[n]. Movement of tap-weight adjustment a function of correlation between u[n] and u[n-1].
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