[HTML][HTML] Deep networks for system identification: a survey
Deep learning is a topic of considerable current interest. The availability of massive data
collections and powerful software resources has led to an impressive amount of results in …
collections and powerful software resources has led to an impressive amount of results in …
[PDF][PDF] Training neural network method modification for forward error propagation based on adaptive components
The work is devoted to the development of a training algorithm for forward propagation
neural networks, based on the backpropagation algorithm, through the use of adaptive …
neural networks, based on the backpropagation algorithm, through the use of adaptive …
NARX identification using derivative-based regularized neural networks
LH Peeters, GI Beintema, M Forgione… - 2022 IEEE 61st …, 2022 - ieeexplore.ieee.org
This work presents a novel regularization method for the identification of Nonlinear
Autoregressive eXogenous (NARX) models. The regularization method promotes the …
Autoregressive eXogenous (NARX) models. The regularization method promotes the …
Finite impulse response design based on two‐level transpose Vedic multiplier for medical image noise reduction
Medical signal processing requires noise and interference‐free inputs for precise
segregation and classification operations. However, sensing and transmitting wireless …
segregation and classification operations. However, sensing and transmitting wireless …
Gradient boosting regression trees for nonlinear delay identification in a polymer extrusion process
Modern polymer extrusion processes, such as pipe productions, usually consist of several
interconnected processing steps exhibiting nonlinear behavior. To support the operators at …
interconnected processing steps exhibiting nonlinear behavior. To support the operators at …
Beans to Bytes: Grey-Box Nonlinear System Identification Using Hybrid Physics-Neural Network Models
M Pronk - 2024 - dspace.mit.edu
The advancement of neural networks in the last several years has yielded some astonishing
results. However, the applicability to system identification and modelling dynamical systems …
results. However, the applicability to system identification and modelling dynamical systems …
[PDF][PDF] Nonlinear State-Space Estimation using Derivative-Based Regularized Neural Networks
LH Peeters - 2022 - pure.tue.nl
This work presents a novel regularization method for the identification of Nonlinear State-
Space (NLSS) models. The proposed method promotes the exponential decay of the …
Space (NLSS) models. The proposed method promotes the exponential decay of the …