2005
DOI: 10.1155/asp.2005.1229
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Modified Clipped LMS Algorithm

Abstract:

Abstract

A new algorithm is proposed for updating the weights of an adaptive filter. The proposed algorithm is a modification of an existing method, namely, the clipped LMS, and uses a three-level quantization ( ) scheme that involves the threshold clipping of the input signals in the filter weight update formula. Mathematical analysis shows the convergence of the filter weights to the optimum Wiener filter weights. Also, … Show more

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Cited by 10 publications
(6 citation statements)
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References 21 publications
(27 reference statements)
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“…To decrease the computational complexity, we can get the sign algorithm as the most famous variant of the standard LMS algorithm [13]. The only difference between the sign algorithm and the standard LMS algorithm lies in the usage of sign function applied to the error signal in the weight coefficients updating process.…”
Section: The Proposed Delayed Dual Sign Lms Algorithmmentioning
confidence: 99%
“…To decrease the computational complexity, we can get the sign algorithm as the most famous variant of the standard LMS algorithm [13]. The only difference between the sign algorithm and the standard LMS algorithm lies in the usage of sign function applied to the error signal in the weight coefficients updating process.…”
Section: The Proposed Delayed Dual Sign Lms Algorithmmentioning
confidence: 99%
“…The computation of SLMS algorithm is greatly reduced especially when eðnÞ is equal to zero, and at the same time this algorithm does not introduce any operations [12]. The prominent feature of SLMS algorithm is utilization of the quantization scheme to reduce the computation complexity.…”
Section: The Proposed Modified Sign-error Lms Algorithmmentioning
confidence: 99%
“…An approximation of the NLMS similar to the clipped LMS algorithm [26][27][28] is proposed. This approach, termed low-complexity normalized LMS (LCNLMS), is suitable for efficient hardware implementation and maintains the fast convergence of the NLMS.…”
Section: Adaptation Of Complex-gain Lut Predistortersmentioning
confidence: 99%
“…Similarly to (27), the LCNLMS can also be applied to the linearly interpolated case, leading to the following update equations:…”
Section: Updating a Linearly-interpolated Lutmentioning
confidence: 99%