Often, the notation for the step size is µ. The maxstep function of dsp.LMSFilter object determines the maximum step size suitable for each LMS adaptive filter algorithm that ensures that the filter converges to a solution. In this case, the resulting filter might not be stable.Īs a rule of thumb, smaller step sizes improve the accuracy with which the filter converges to match the characteristics of the unknown system, at the expense of the time it takes to adapt. Fitting a curve to data is a common technique used in Artificial intelligence and Machine learning models to predict the values of various attributes. A step size that is too large might cause the adapting filter to diverge and never reach convergence. MATLAB fit method can be used to fit a curve or a surface to a data set. To validate a model we need a scoring function (see Metrics and scoring: quantifying the quality of predictions), for example accuracy for classifiers.The proper way of choosing multiple hyperparameters of an estimator are of course grid search or similar methods (see Tuning the hyper-parameters of an estimator) that select the hyperparameter with the maximum score. is an example of a closed-loop tracking device. Adaptive front end correction is an example of adaptive filtering, while code tracking using DLL, which is closely related to PLL. A step size that is too small increases the time for the filter to converge on a set of coefficients. pseudo-noise (PN) code tracking in direct-sequence spread-spectrum (DS/SS) systems using delay-locked loops (DLL). LMS-like algorithms have a step size that determines the amount of correction applied as the filter adapts from one iteration to the next.
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