System identification

Related lectures (7)

Explores system modeling and parameters identification through step response experiments in control systems.

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Delves into the continual learning of representational models in brain intelligence, emphasizing rapid adaptation to unstructured environments.

Explores dynamical modeling, decoding, and control of brain network activity for personalized therapy.

Explores process unit modeling, balance equations, degrees of freedom, and flowsheet solving.

Covers the application of optimal control in parameter identification and the trade-off between the model and measurements.

Covers the principles and applications of linear regression, focusing on building a simple model to make suggestions.

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