Using uncertain prior knowledge to improve identified nonlinear dynamic models (original) (raw)
2011, Journal of Process Control
AI-generated Abstract
The paper presents an approach to enhance the performance of nonlinear dynamic models through the incorporation of uncertain prior knowledge. This approach utilizes a framework based on NARX polynomial models and addresses the challenges of system identification in scenarios where data uncertainty is inevitable. By contrasting various estimation techniques, including least-squares methods and proposed weighted estimators, the findings highlight how informed prior knowledge can lead to improved parameter estimation and model accuracy, particularly in practical applications such as electrical and hydraulic systems.
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