Lms Algorithm Matlab Pdf 15

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Least mean squares (LMS) algorithms represent the simplest and most easily applied adaptive algorithms. The recursive least squares (RLS) algorithms, on the other hand, are known for their excellent performance and greater fidelity, but they come with increased complexity and computational cost. In performance, RLS approaches the Kalman filter in adaptive filtering applications with somewhat reduced required throughput in the signal processor. Compared to the LMS algorithm, the RLS approach offers faster convergence and smaller error with respect to the unknown system at the expense of requiring more computations.

The LMS filters adapt their coefficients until the difference between the desired signal and the actual signal is minimized (least mean squares of the error signal). This is the state when the filter weights converge to optimal values, that is, they converge close enough to the actual coefficients of the unknown system. This class of algorithms adapt based on the error at the current time. The RLS adaptive filter is an algorithm that recursively finds the filter coefficients that minimize a weighted linear least squares cost function relating to the input signals. These filters adapt based on the total error computed from the beginning.

The LMS filters use a gradient-based approach to perform the adaptation. The initial weights are assumed to be small, in most cases very close to zero. At each step, the filter weights are updated based on the gradient of the mean square error. If the gradient is positive, the filter weights are reduced, so that the error does not increase positively. If the gradient is negative, the filter weights are increased. The step size with which the weights change must be chosen appropriately. If the step size is very small, the algorithm converges very slowly. If the step size is very large, the algorithm converges very fast, and the system might not be stable at the minimum error value. To have a stable system, the step size μ must be within these limits:

In cases where the error value might come from a spurious input data point or points, the forgetting factor lets the RLS algorithm reduce the significance of older error data by multiplying the old data by the forgetting factor.

lms = dsp.LMSFilter returns an LMS filter object, lms, that computes the filtered output, filter error, and the filter weights for a given input and a desired signal using the least mean squares (LMS) algorithm.

The input, x can be a variable-size signal. You can change the number of elements in the column vector even when the object is locked. The System object locks when you call the object to run its algorithm.

The input, d can be a variable-size signal. You can change the number of elements in the column vector even when the object is locked. The System object locks when you call the object to run its algorithm.

Increase the frame size of the desired signal. Even though this increases the computation involved, the LMS algorithm now has more data that can be used for adaptation. With 1000 samples of signal data and a step size of 0.2, the coefficients are aligned closer than before, indicating an improved convergence.

Increase the number of data samples further by inputting the data through iterations. Run the algorithm on 4000 samples of data, passed to the LMS algorithm in batches of 1000 samples over 4 iterations.

Create two dsp.LMSFilter objects, with one set to the LMS algorithm, and the other set to the normalized LMS algorithm. Choose an adaptation step size of 0.2 and set the length of the adaptive filter to 13 taps.

Pass the primary input signal x and the desired signal d to both the variations of the LMS algorithm. The variables e1 and e2 represent the error between the desired signal and the output of the normalized and nonnormalized filters, respecitvely.

In the standard and normalized variations of the LMS adaptive filter, coefficients for the adapting filter arise from the mean square error between the desired signal and the output signal from the unknown system. The sign-data algorithm changes the mean square error calculation by using the sign of the input data to change the filter coefficients.

with vector w containing the weights applied to the filter coefficients and vector x containing the input data. The vector e is the error between the desired signal and the filtered signal. The objective of the SDLMS algorithm is to minimize this error. Step size is represented by μ.

Note: How you set the initial conditions of the sign-data algorithm profoundly influences the effectiveness of the adaptation process. Because the algorithm essentially quantizes the input signal, the algorithm can become unstable easily.

In System Identification of FIR Filter Using LMS Algorithm, you constructed a default filter that sets the filter coefficients to zeros. In most cases that approach does not work for the sign-data algorithm. The closer you set your initial filter coefficients to the expected values, the more likely it is that the algorithm remains well behaved and converges to a filter solution that removes the noise effectively.

When dsp.LMSFilter runs, it uses far fewer multiplication operations than either of the standard LMS algorithms. Also, performing the sign-data adaptation requires only multiplication by bit shifting when the step size is a power of two.

Although the performance of the sign-data algorithm as shown in this plot is quite good, the sign-data algorithm is much less stable than the standard LMS variations. In this noise cancellation example, the processed signal is a very good match to the input signal, but the algorithm could very easily grow without bound rather than achieve good performance.

Note: How you set the initial conditions of the sign-error algorithm profoundly influences the effectiveness of the adaptation process. Because the algorithm essentially quantizes the error signal, the algorithm can become unstable easily.

In System Identification of FIR Filter Using LMS Algorithm, you constructed a default filter that sets the filter coefficients to zeros. In most cases that approach does not work for the sign-error algorithm. The closer you set your initial filter coefficients to the expected values, the more likely it is that the algorithm remains well behaved and converges to a filter solution that removes the noise effectively.

When the sign-error LMS algorithm runs, it uses far fewer multiplication operations than either of the standard LMS algorithms. Also, performing the sign-error adaptation requires only bit shifting multiples when the step size is a power of two.

Although the performance of the sign-error algorithm as shown in this plot is quite good, the sign-error algorithm is much less stable than the standard LMS variations. In this noise cancellation example, the adapted signal is a very good match to the input signal, but the algorithm could very easily become unstable rather than achieve good performance.

Changing the weight initial conditions (InitialConditions) and mu (StepSize), or even the lowpass filter you used to create the correlated noise, can cause noise cancellation to fail and the algorithm to become useless.

Vector w contains the weights applied to the filter coefficients and vector x contains the input data. The vector e is the error between the desired signal and the filtered signal. The objective of the SSLMS algorithm is to minimize this error.

How you set the initial conditions of the sign-sign algorithm profoundly influences the effectiveness of the adaptation process. Because the algorithm essentially quantizes the input signal and the error signal, the algorithm can become unstable easily.

To prepare the dsp.LMSFilter object for processing, set the initial conditions of the filter weights (InitialConditions) and mu (StepSize). As noted earlier in this section, the values you set for coeffs and mu determine whether the adaptive filter can remove the noise from the signal path. In System Identification of FIR Filter Using LMS Algorithm, you constructed a default filter that sets the filter coefficients to zeros. Usually that approach does not work for the sign-sign algorithm.

The closer you set your initial filter coefficients to the expected values, the more likely it is that the algorithm remains well behaved and converges to a filter solution that removes the noise effectively. For this example, you start with the coefficients used in the noise filter (filt.Numerator), and modify them slightly so the algorithm has to adapt.

When dsp.LMSFilter runs, it uses far fewer multiplication operations than either of the standard LMS algorithms. Also, performing the sign-sign adaptation requires only bit shifting multiples when the step size is a power of two.

Although the performance of the sign-sign algorithm as shown in this plot is quite good, the sign-sign algorithm is much less stable than the standard LMS variations. In this noise cancellation example, the adapted signal is a very good match to the input signal, but the algorithm could very easily become unstable rather than achieve good performance. 2b1af7f3a8