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Data-based construction of feedback-corrected nonlinear prediction model using feedback neural networks
2001
Control Engineering Practice
We propose to fit a recurrent feedback neural network structure to input-output data through prediction error minimization. The recurrent feedback neural network structure takes the form of a nonlinear state estimator, which can compactly represent a multivariable dynamic system with stochastic inputs. The inclusion of the feedback error term as an input to the model allows the user to update the model based on feedback measurements in real-time uses. The model can be useful in a variety of
doi:10.1016/s0967-0661(01)00050-8
fatcat:t5di7wxebngrbkaioaz4aobmja