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

By Forester W. Isen

This publication is quantity IV of the sequence DSP for MATLABв„ў and LabVIEWв„ў. quantity IV is an introductory therapy of LMS Adaptive Filtering and purposes, and covers fee capabilities, functionality surfaces, coefficient perturbation to estimate the gradient, the LMS set of rules, reaction of the LMS set of rules to narrow-band indications, and numerous topologies comparable to ANC (Active Noise Cancelling) or procedure modeling, Noise Cancellation, Interference Cancellation, Echo Cancellation (with unmarried- and dual-H topologies), and Inverse Filtering/Deconvolution. the complete sequence contains 4 volumes that jointly conceal uncomplicated electronic sign processing in a pragmatic and obtainable demeanour, yet which still contain all crucial origin arithmetic. because the sequence name implies, the scripts (of which there are greater than two hundred) defined within the textual content and provided in code shape (available through the net 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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Additional resources for DSP for MATLAB and LabVIEW IV: LMS Adaptive Filtering

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7). • The method of steepest descent is not necessarily one which takes the shortest and/or fastest path to the minimum of the cost function. This is a function of the shape of the performance surface. 18 CHAPTER 1. INTRODUCTION TO LMS ADAPTIVE FILTERING Method of Steepest Descent Modified In the script LVxModelPtswLine, the rate at which the slower coefficient converges is modified by the multiplier yMu2MuRatio, leading to a more optimal path from starting point to minimum value of cost function.

The weighting function chosen for the script LVxGradientViaCP is unregulated, meaning that account has not been taken for all input conditions. 0001,75) causes divergence because the weighting function is used with a very large starting value of x = 400, which causes the initial update term (considering the value of the weighting function, which is 16,001) to be too large. It should be obvious that the size of the update term must be carefully controlled to maintain adequate convergence speed and stability.

C2Strt, N oI ts, tstDT ype, CosF rq) embodies such an algorithm, where P C1 and P C2 are the Plant coefficients to be modeled, c1Strt and c2Strt are the initial guesses or starting values for the coefficient estimates, NoI ts is the number of iterations to perform, Mu has the usual meaning, and tstDType selects the type of test signal. 11. LMS ADAPTIVE FIR SUMMARY 37 tstDType as 0 for white noise, 1 for a unit step (DC), 2 for the Nyquist Limit frequency, 3 for the half-band frequency, and 4 for a cosine of frequency CosFrq (may be passed as [] if a cosine is not being used as a test signal).

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