Variants - Leaky LMS Algorithm Leaky LMS Algorithm Convergence of tap-weight error modes dependent on conditioning of input χ(R For

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Unformatted text preview: Leaky LMS Algorithm 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 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 based Algorithms ¡ Sign-error algorithm: ¡ Sign-data algorithm: ¡ Sign-sign algorithm: Sign Sign-based Algorithms 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. Affine Affine Projection Algorithm Projection Algorithm ¡ Tap-weight adjustment of NLMS: w[n+1] – w[n] in the direction of u[n]. the direction of u[n]....
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This note was uploaded on 12/02/2011 for the course AR 107 taught by Professor Gracegraham during the Fall '11 term at Montgomery College.

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Variants - Leaky LMS Algorithm Leaky LMS Algorithm Convergence of tap-weight error modes dependent on conditioning of input χ(R For

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