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- W2162421989 abstract "In this paper, we discuss the implementation of a neural network feedback loop, a state estimator and a state feedback controller, that trains online to overcome variations between the a priori model dynamics and the true system dynamics. The technique uses the previously developed neuro-observer, an extended Kalman filter augmented with a neural network, and a model reference adaptive control law. Both neural networks, the state estimator's and the controller's, are trained using extended Kalman filter training paradigms. The paper also includes a discussion of parameter selections for the neural networks and their training paradigms." @default.
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- W2162421989 date "2002-12-24" @default.
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- W2162421989 title "Artificial neural network feedback loop with on-line training" @default.
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- W2162421989 doi "https://doi.org/10.1109/isic.1996.556254" @default.
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