Multi-Innovation Iterative Identification Algorithms for CARMA Tumor Models

Since system identification plays a crucial role in controlling systems, it is essential to have access to appropriate identification methods. In this paper, two novel identification methods are proposed for estimating Controlled Auto-Regressive Moving Average (CARMA) systems: the multi-innovation gradient-based iterative algorithm and the two-stage multi-innovation gradient-based iterative algorithm. Our primary objective is to estimate the unknown parameters of a tumor model using these methods. To evaluate the effectiveness of the proposed methods, various factors are considered, such as convergence rate and estimation error. By conducting simulations, the practical applicability and performance of the introduced algorithms are demonstrated. The obtained results are presented through tables and figures, providing a comprehensive analysis of the estimation outcomes. The multi-innovation gradient-based iterative algorithm and the two-stage multi-innovation gradient-based iterative algorithm offer valuable contributions to the field of system identification, particularly in the context of CARMA systems. These methods offer an innovative approach to estimate the parameters of complex systems, specifically focusing on tumor models. The convergence rate and estimation error analysis highlight the reliability and accuracy of the proposed methods, indicating their potential for practical implementation. In conclusion, this paper presents novel identification methods for estimating CARMA systems in the context of tumor models. The proposed algorithms demonstrate promising results in terms of convergence rate and estimation accuracy. These findings contribute to the development of effective and reliable identification techniques, offering valuable insights for controlling and understanding complex systems.