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The newest entry in the AI category is titled "Agnostic system identification for model-based reinforcement learning".
We argue that system identification for controller design should be adaptive and therefore fault tolerant, while system identification for fault detection should be non-adaptive.
Many iterative approaches in the field of system identification for control have been developed.
The augmented, iterated Kalman smoother is applied to system identification for inverse problems in evolutionary differential equations.
In the last decades, many iterative approaches in the field of system identification for control have been proposed.
System identification for non-linear dynamical systems could find use in many applications such as condition monitoring, finite element model validation and determination of stability.
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The improved approach can be used for system identification of both linear and nonlinear structures.
A sparse system identification algorithm for network echo cancellation is presented.
The random decrement technique and spectral analysis are commonly invoked system identification schemes for this problem.
An online intelligent system identification approach for the mold cooling process is developed and tested.
A broadly-applicable, control-relevant system identification methodology for nonlinear restricted complexity models (RCMs) is presented.
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