• Kiavash Hossein Sadeghi Department of Electrical Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr 75169, Iran
  • Abolhassan Razminia Department of Electrical Engineering, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr 75169, Iran
  • Abolfaz Simorgh Department of Aerospace Engineering, Universidad Carlos III de Madrid, 28911 Leganés, Spain



Gradient Based Iterative Algorithms, 2-Stage Identification, System Identification, Parameter Estimation, Tumor Model


The article investigates the parameter estimation for controlled auto-regressive moving average models with gradient based iterative approach and two-stage gradient based iterative approach. Since deriving a new model for tumor model is substantial, introduced system identification algorithms are used in order to estimate parameters of a specific nonlinear tumor model. Besides, in order to estimate tumor model a collection of output and input data is taken from the nonlinear system. Apart from that, effectiveness of the identification algorithms such as convergence rate and estimation error is depicted through various tables and figures. Finally, it is shown that the two stage approach has higher identification efficacy.


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How to Cite

Sadeghi, K. H., Razminia, A., & Simorgh, A. . (2024). CONTROL AND IDENTIFICATION OF CONTROLLED AUTO-REGRESSIVE MOVING AVERAGE (CARMA) FORM OF AN INTRODUCED SINGLE-INPUT SINGLE-OUTPUT TUMOR MODEL. International Journal of Engineering Technologies and Management Research, 11(2), 1–18.