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Found 6 papers, 5 papers with code
Functional observability and target state estimation in large-scale networks
The quantitative understanding and precise control of complex dynamical systems can only be achieved by observing their internal states via measurement and/or estimation.
Identification of NARX Models for Compensation Design
Finally, the experimental example is a pneumatic valve that presents a variety of nonlinearities, including hysteresis.
Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness
The exploding and vanishing gradient problem has been the major conceptual principle behind most architecture and training improvements in recurrent neural networks (RNNs) during the last decade.
On the smoothness of nonlinear system identification
We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems.
Lasso Regularization Paths for NARMAX Models via Coordinate Descent
We propose a new algorithm for estimating NARMAX models with $L_1$ regularization for models represented as a linear combination of basis functions.
"Parallel Training Considered Harmful?": Comparing series-parallel and parallel feedforward network training
Neural network models for dynamic systems can be trained either in parallel or in series-parallel configurations.
