Neural networks for modelling and control of dynamic systems: M. Nørgaard, O. Ravn, N. K. Poulsen and L. K. Hansen. Springer-Verlag, London Berlin Heidelberg, 2000, pp. xiv+246

By Jonas Sjöberg. 2004

364 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/849753480dcd03eb5e55b56c186d810564898866

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In Control and Systems Engineering

Citations in the corpus

A Microgrid Energy Management System Based on the Rolling Horizon Strategy (2013, background): “Therefore, the solar power will be a value between these bounds, which are as follows: (3) (4) where is the solar panel efficiency, is the total plant surface and is the irradiance perpendicular to the panel’s plane.”
Variational Bayesian learning of nonlinear hidden state-space models for model predictive control (2009, background): “Many such schemes exists, including direct inverse control, optimal control, and feedforward control [22].”
Long-term time series prediction with the NARX network: An empirical evaluation (2008, background): “NARX [26,30,33] is an important class of discrete-time nonlinear systems that can be mathematically represented as”
Virtual reference feedback tuning: a direct method for the design of feedback controllers (2002, background): “A similar idea, though in a special setting, has also been used in the context of control with neural networks, see [17].”
Q-clouds: managing performance interference effects for QoS-aware clouds (2010, methodology): “For example, if the control being applied requires capturing system dynamics and nonlinearities, neural networks might be applicable to better estimate the relationship in Equation 1 [24].”
Gaussian process model based predictive control (2004, neutral): “[16], fuzzy models e.”
A review on model-based diagnosis methodologies for PEMFCs (2013, background): “Moreover, by retaining the non-linear mapping features of the static networks, the RNN are suitable for black-box nonlinear dynamic modelling [41,42].”