Discrete wavelet transform-based RI adaptive algorithm for system identification

Abstract

In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCTLMS), discrete-wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions.

A comprehensive overview of the ADALINE method applied to rapid voltage sags detection in multi-motors drive systems

“…𝑒(𝑘) = 𝑦 𝑚𝑒𝑠 (𝑘) − 𝑦(𝑘) is the error between estimated and measured outputs. For the weight adaptation algorithm, we choose the recursive least squares (RLS) algorithm [21], [22]. It is a quadratic method which consists in minimizing a quadratic function of error e(k) between the measured signal and the model.…”

NARX-based water quality index model of Air Busuk River using chemical parameter measurements

“…The model is assessed via regression, time-series response, and error histogram. Autocorrelation function (ACF) test [37] and cross-correlation function (CCF) [38] test is performed to verify that the residuals and predicted output is not correlated with each other. Development of the WQI model is performed in MATLAB.…”