文件名称:adaptive-signal-arithmetic
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Some algorithms of variable step size LMS adaptive filtering are studied.The VS—LMS algorithm is improved.
Another new non-linear function between肛and e(/ t)is established.The theoretic analysis and computer
simulation results show that this algorithm converges more quickly than the origina1.Furthermore,better antinoise
property is exhibited under Low—SNR environment than the original one.-variable step size of a LMS daptive filtering are studied. The VS-LMS algorithm is improved. Another new non-linear function between anus and e (/ t) is established. The theoretic analysis and computer simulatio n results show that this algorithm converges mo 're quickly than the origina1. Furthermore, better antinoise property is exhibited under L ow- SNR environment than the original one.
Another new non-linear function between肛and e(/ t)is established.The theoretic analysis and computer
simulation results show that this algorithm converges more quickly than the origina1.Furthermore,better antinoise
property is exhibited under Low—SNR environment than the original one.-variable step size of a LMS daptive filtering are studied. The VS-LMS algorithm is improved. Another new non-linear function between anus and e (/ t) is established. The theoretic analysis and computer simulatio n results show that this algorithm converges mo 're quickly than the origina1. Furthermore, better antinoise property is exhibited under L ow- SNR environment than the original one.
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vs
lms
variable
step
size
LMS
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matlab
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LMS
matlab
SNR
for
LMS
algorithm
step
lms
snr
lms
under-detemined
vs
lms
variable
step
size
LMS
algorithm
matlab
robust
signal
LMS
matlab
SNR
for
LMS
algorithm
step
lms
snr
lms
under-detemined
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adaptive-signal-arithmetic.doc