Download e-book for iPad: DSP for MATLAB and LabVIEW IV: LMS Adaptive Filtering by Forester W. Isen

By Forester W. Isen

ISBN-10: 1598298992

ISBN-13: 9781598298994

ISBN-10: 159829900X

ISBN-13: 9781598299007

This publication is quantity IV of the sequence DSP for MATLABв„ў and LabVIEWв„ў. quantity IV is an introductory remedy of LMS Adaptive Filtering and purposes, and covers rate features, functionality surfaces, coefficient perturbation to estimate the gradient, the LMS set of rules, reaction of the LMS set of rules to narrow-band signs, and diverse topologies reminiscent of ANC (Active Noise Cancelling) or method modeling, Noise Cancellation, Interference Cancellation, Echo Cancellation (with unmarried- and dual-H topologies), and Inverse Filtering/Deconvolution. the full sequence contains 4 volumes that jointly conceal easy electronic sign processing in a realistic and obtainable demeanour, yet which still comprise all crucial beginning arithmetic. because the sequence identify implies, the scripts (of which there are greater than 2 hundred) defined within the textual content and provided in code shape (available through the web at www.morganclaypool.com/page/isen) will run on either MATLABв„ў and LabVIEWв„ў. desk of Contents: creation To LMS Adaptive Filtering / utilized Adaptive Filtering

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Extra info for DSP for MATLAB and LabVIEW IV: LMS Adaptive Filtering

Example text

The method of steepest descent, which uses the negative of the gradient to estimate the next point to move to on the performance surface, moves along the direction which reduces the MSE by the largest amount for a given very small amount of movement. In Fig. 7, the performance surface is much like a river that has relatively steep banks which slope not only down to the river, but which slope slightly downward along the direction of river flow. If a ball were released from the top of such a river bank, its direction of travel would be determined by gravity.

22) We can derive an estimate for the partial derivative of ApproxMSE with respect to, say, c1 , (as in previous examples) as ∂(ApproxMSE[n]) = 2((P C1 − c1 [n])x[n + 1]... 24) 24 CHAPTER 1. 25) may be interpreted as saying that the partial derivative of the scalar error squared at sample time n with respect to the kth tap coefficient at sample time n is negative two times the scalar error at sample time n, E[n], multiplied by the signal value at the kth tap at sample time n. 26) where ci [n] represents the tap coefficient of tap index i and iteration n, μ is a scalar constant which scales the overall magnitude of the update term, E[n] represents the scalar error at iteration n, and xi [n] is the signal value at tap i at iteration n.

For many problems, however, such is not the case. For the type of system shown in Fig. 10, the Plant’s impulse response is unknown, and the consequence of having only the Plant’s output to work with is that we do not have a true measure of MSE by which to estimate the gradient. A true MSE measure of coefficient misadjustment would be T rueMSE[n] = ((P C1 − c1 [n]) · x[n + 1])2 + ... ((P C2 − c2 [n]) · x[n])2 This cost function has a true global or unimodal minimum since it can only be made equal to zero when both coefficients are perfectly converged to the plant values.

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DSP for MATLAB and LabVIEW IV: LMS Adaptive Filtering by Forester W. Isen


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