System Identification of FIR Filters

Rao, Sudheesh Kannur Vasudeva; Kiran, Kiran; Kumar, Naveen; Mahadevaswamy, Mahadevaswamy

doi:10.55708/js0104010

Cited by 1 publication

(1 citation statement)

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“…An accurate method of RLS is a recursive method that is used in the adaptive filtering. Despite its high accuracy and fast convergence, RLS has the disadvantage of having a high computational complexity, in addition to requiring predefined information and conditions for the coefficient updating operation [28]. This makes RLS method less popular compared to LMS.…”

Section: B Adaptive Filtering Algorithms 1) Lms Algorithmmentioning

confidence: 99%

Improved LMS Performance for System Identification Application

Mamdoh Abdulatef,

Rami Alomari,

Ahmed Mahmod

2024

IJCDS

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The adaptive filtering algorithm of Normalized Least Mean Square (NLMS) is known to be highly efficient in terms of requiring less number of iterations compared to the reference Least Mean Square (LMS) method, at the cost of having increased computations. This is because, while a fixed value is assigned for the step size in LMS method, the step size in NLMS is continuously updated to a new value with each iteration. The performance of the gradient descent method of LMS is found to be highly dependent on the step size value assigned at the beginning of the iterations. Throughout the literature, the whole range of LMS step size within which stability is assured is usually suggested and studied, while the selection of the most suitable value of the step size within this range is still not thoroughly studied. In this work, a new method to help specifying the exact value of step size in LMS is proposed. This method is resembled by using the step size value assigned in the first iteration in NLMS method to set the value of the step size in LMS. This is found through the results to be highly effective in approaching NLMS behavior without having to increase computational burden. The performance of the proposed method is evaluated using the Mean Square Error (MSE), Weight Difference (WD) and absolute error after 1000 iterations. Relying on this way to specify the LMS step size can provide simplicity, accuracy and high convergence speed, not only for system identification, but also for many other adaptive filtering applications.

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Section: B Adaptive Filtering Algorithms 1) Lms Algorithmmentioning

confidence: 99%