The paper introduces a novel direct approach to system identification of dynamic networks with missing data using maximum likelihood estimation. It addresses challenges in estimating parameters due to singular probability density functions and offers insights into transforming these networks for better estimations. The study compares the proposed direct approach with an indirect method, highlighting advantages such as improved estimation accuracy and reduced sensitivity to initialization strategies.
The content discusses the technical challenges in controlling large-scale complex systems and the limitations of existing methods in scaling with increasing complexity. It emphasizes the need for systematic approaches for designing and analyzing distributed control structures in various applications. The study delves into model-based controller design methodologies, emphasizing the importance of efficient modeling for scalable control methods.
Furthermore, it explores different approaches to estimate parameters in interconnected systems, comparing direct and indirect methods. The analysis reveals drawbacks of the indirect approach, such as unstable models and increased variance in estimates. In contrast, the proposed direct method shows promise in obtaining accurate estimates by observing fewer variables while maintaining stability.
The paper includes a numerical experiment illustrating properties of the proposed method using a network of three systems. It discusses results from both direct and indirect approaches, showcasing improvements in estimation accuracy when observing additional signals. The study concludes by suggesting future work on applying the approach to larger network architectures and exploring different optimization methods.
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