48th Midwest Symposium on Circuits and Systems, 2005. 2005
DOI: 10.1109/mwscas.2005.1594108
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Computationally-efficient methods for blind adaptive equalization

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Cited by 7 publications
(2 citation statements)
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“…The error function is used to update the weights and the similar process takes place till the error is reduces to the minimum. Learning of the FLANN may be taken as interpolation or approximation of a function which is continuous and multivariate by error function approximation [7] [8]. If b is the training patterns denoted by (X i : Y i ), 1 i b and the weight matrix be W. At time equal to ith second if N dimensional input pattern is: Xi=xi 1 , xi 2 .…”
Section: Flannmentioning
confidence: 99%
“…The error function is used to update the weights and the similar process takes place till the error is reduces to the minimum. Learning of the FLANN may be taken as interpolation or approximation of a function which is continuous and multivariate by error function approximation [7] [8]. If b is the training patterns denoted by (X i : Y i ), 1 i b and the weight matrix be W. At time equal to ith second if N dimensional input pattern is: Xi=xi 1 , xi 2 .…”
Section: Flannmentioning
confidence: 99%
“…As mentioned before LMS algorithm is built around a transversal filter, which is responsible for performing the filtering process. A weight control mechanism responsible for performing the adaptive control process on the tape weight of the transversal filter [9] as illustrated in Figure 4. Filtering process, which involve, computing the output…”
Section: A Least Mean Squares Algorithm (Lms)mentioning
confidence: 99%