Deep learning is a topic of considerable interest today. Since it deals with estimating - or learning - models, there are connections to the area of System Identification developed in the Automatic Control community. Such connections are explored and exploited in this contribution. It is stressed that common deep nets such as feedforward and cascadeforward nets are nonlinear ARX (NARX) models, and can thus be easily incorporated in System Identification code and practice. The case of LSTM nets is an example of NonLinear State-Space (NLSS) models.Koen Tiels: "Was with the dept in Uppsala. Now at Dept of Mechanical Engineering, Eindhoven University of Technology"</p
In analyzing and assessing the condition of dynamical systems, it is necessary to account for nonlin...
Modern deep neural networks are being widelyexploited to solve challenging learning tasks, inc...
AbstractSystem identification is the process of deducing a mathematical model of the internal dynami...
Deep learning is a topic of considerable interest today. Since it deals with estimating - or learnin...
Deep state space models (SSMs) are an actively researched model class for temporal models developed ...
The identification of a nonlinear dynamic model is an open topic in control theory, especially from ...
Modeling dynamical systems is important in many disciplines, such as control, robotics, or neurotech...
Although many mathematical and analytical techniques have been presented to control and identify the...
The authors review some of the basic system identification machinery to reveal connections with neur...
Machine learning has been applied to sequential data for a long time in the field of system identifi...
The identification of a nonlinear dynamic model is an open topic in control theory, especially from ...
The identification and analysis of high dimensional nonlinear systems is obviously a challenging tas...
The present paper treats the identification of nonlinear dynamical systems using Koopman-based deep ...
Certain properties of the back-propagation neural network have been found to be potentially useful i...
The paper presents two learning methods for nonlinear system identification. Both methods employ neu...
In analyzing and assessing the condition of dynamical systems, it is necessary to account for nonlin...
Modern deep neural networks are being widelyexploited to solve challenging learning tasks, inc...
AbstractSystem identification is the process of deducing a mathematical model of the internal dynami...
Deep learning is a topic of considerable interest today. Since it deals with estimating - or learnin...
Deep state space models (SSMs) are an actively researched model class for temporal models developed ...
The identification of a nonlinear dynamic model is an open topic in control theory, especially from ...
Modeling dynamical systems is important in many disciplines, such as control, robotics, or neurotech...
Although many mathematical and analytical techniques have been presented to control and identify the...
The authors review some of the basic system identification machinery to reveal connections with neur...
Machine learning has been applied to sequential data for a long time in the field of system identifi...
The identification of a nonlinear dynamic model is an open topic in control theory, especially from ...
The identification and analysis of high dimensional nonlinear systems is obviously a challenging tas...
The present paper treats the identification of nonlinear dynamical systems using Koopman-based deep ...
Certain properties of the back-propagation neural network have been found to be potentially useful i...
The paper presents two learning methods for nonlinear system identification. Both methods employ neu...
In analyzing and assessing the condition of dynamical systems, it is necessary to account for nonlin...
Modern deep neural networks are being widelyexploited to solve challenging learning tasks, inc...
AbstractSystem identification is the process of deducing a mathematical model of the internal dynami...