Iterative identification algorithm for tumor model using controlled ARMA model

Document Type : Research Article

Authors

1 Department of Electrical Engineering, Faculty of Intelligent Systems Engineering and data science, Persian Gulf University, Bushehr 75169, Iran

2 Process Control Laboratory, Faculty of Natural Sciences and Engineering, Abo Akademi University, Turku, Finland

Abstract

Since system identification of a tumor model is a primary need for controlling tumor model system, accessing suitable and applicable identification methods is a necessary object. In this paper, firstly, for estimating controlled auto-regressive moving average (CARMA) systems, two  identification methods, namely generalized projection algorithm (GPA) and two-stage GPA (2S-GPA), are introduced and presented in order to estimate unknown parameters of a specific and vital tumor model. Furthermore, effectiveness of such methods, like convergence rate and estimation error, are discussed and considered. The introduced algorithms are simulated to prove these methods effectiveness, and data derived from the simulations are depicted through tables and figures.

Keywords

Journal of Mathematical Modeling
Volume 13, Issue 1
March 2025
Pages 69-83

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