“…The objective of an adaptive filter is to generate the output signal y ( k ) as close as possible to the desired signal d ( k ) . To do this, the adaptive filter adjusts its coefficient w( k ) at each sampling time k by [12] 1 µ…”
“…where k is the discrete time index, T denotes to transpose, and adaptive Finite Impulse Response (FIR) filter coefficient vector defined by [12] , , … … … … . The objective of an adaptive filter is to generate the output signal y ( k ) as close as possible to the desired signal d ( k ) .…”
Abstract-This paper presents a new method for adaptive step size for Affine Projection Algorithm (APA). The proposed algorithm is based on an absolute mean of estimation current and prior error vector. It is called Absolute Mean Error Adaptive Step Size Affine Projection Algorithm (AMASSAPA). The main goal of this algorithm is performance enhancement of adaptive colored filtering system in terms of fast convergence time. The proposed adaptive step size method begins the learning process with high learning rate value ( µ MAX ) and then , it decays in an exponential profile to its minimum value (µ MIN ) .The proposed algorithm is tested with a colored input signal and the result shows that it has fast convergence time than traditional APA.Index Terms-Adaptive filter, adaptive step size, affine projection algorithm.
“…The objective of an adaptive filter is to generate the output signal y ( k ) as close as possible to the desired signal d ( k ) . To do this, the adaptive filter adjusts its coefficient w( k ) at each sampling time k by [12] 1 µ…”
“…where k is the discrete time index, T denotes to transpose, and adaptive Finite Impulse Response (FIR) filter coefficient vector defined by [12] , , … … … … . The objective of an adaptive filter is to generate the output signal y ( k ) as close as possible to the desired signal d ( k ) .…”
Abstract-This paper presents a new method for adaptive step size for Affine Projection Algorithm (APA). The proposed algorithm is based on an absolute mean of estimation current and prior error vector. It is called Absolute Mean Error Adaptive Step Size Affine Projection Algorithm (AMASSAPA). The main goal of this algorithm is performance enhancement of adaptive colored filtering system in terms of fast convergence time. The proposed adaptive step size method begins the learning process with high learning rate value ( µ MAX ) and then , it decays in an exponential profile to its minimum value (µ MIN ) .The proposed algorithm is tested with a colored input signal and the result shows that it has fast convergence time than traditional APA.Index Terms-Adaptive filter, adaptive step size, affine projection algorithm.
“…represents half of the estimated i -th coordinate of the performance criterion gradient for the LMS algorithm, [1,2,3]. The best bias-to-variance ratio is obtained for the particular step size that turns (15) into an equality.…”
Section: New Vs Lms Adaptive Algorithmmentioning
confidence: 99%
“…Let the first one have the maximal step size value max µ which does not violate the algorithm convergence condition [1,2,3], while the second one is characterized in each iteration by the variable step size ) (k i µ . The analysis from the previous section may now be applied to these two algorithms.…”
Section: New Vs Lms Adaptive Algorithmmentioning
confidence: 99%
“…There is a number of adaptive algorithms, [1,2,3,4,6,8], derived from the conventional LMS algorithm. Variable step-size methods [4,5,6] aim to improve the convergence of the LMS algorithm, while preserving the steady-state performance.…”
-The paper proposes a new adaptive VS LMS algorithm, obtained by combining LMS algorithms with different step sizes without calculating their weighting coefficients. As a criterion for choosing the VS LMS algorithm step size, we take the ratio between the weighting coefficients' bias and variance. Identification of an unknown system in nonstationary noisy environment is performed and simulations with the proposed and other VS LMS algorithms are presented. Simulation results confirm the favorable properties of the proposed algorithm in nonstationary environment with abrupt changes of unknown system parameters.
On the basis of the statistical characteristics of analyte lines and spectral interferences in inductively coupled plasma-atomic emission spectrometry (ICP-AES), the adaptive noise canceling model in the signal-processing technique has been applied to the correction of spectral interferences.Correction results of the simulated and experimental spectra have demonstrated the feasibility and effectiveness of this method. ᭧
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