Javed, ShaziaNazir, GhidaChaudhry, Nazir AhmadAkgul, AliTabassum, Muhammad Farhan2024-12-242024-12-2420231752-28621752-2870https://doi.org/10.1504/IJANS.2023.133733https://hdl.handle.net/20.500.12604/7381The recursive least squares (RLS) methods are extremely used to find the solutions of problems in many areas, such as communication, signal processing, optimisation and control. In this paper the RLS algorithm is modified for parameter estimation of regression models, such as the pseudo-linear ARMA (PS-ARMA) model and output error autoregressive (OEAR) model. The adaptive filtering technique with random input-output is used in the proposed recursive parameter estimation (RPE) algorithm to recursively predict the exact set of parameters for any regression model. The proposed method works by predicting an output signal that is adaptively improved to approximate the desired filter output. The experimental results are provided to prove the effectiveness of the proposed method.eninfo:eu-repo/semantics/closedAccessadaptive filterautoregressive modelparameter estimationrandom signalsArticle417289Q4WOS:00107976330000610.1504/IJANS.2023.133733