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Reference Work Entry In depth
Deep Learning in a System Identification Perspective
The use of deep learning for sequence learning problems and system identification are intimately linked, and interesting opportunities exist on this cross section. The aim of this chapter is to briefly introdu...
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Reference Work Entry In depth
Nonlinear System Identification Using Particle Filters
Particle filters are computational methods opening up for systematic inference in nonlinear/non-Gaussian state-space models. The particle filter constitute the most popular sequential Monte Carlo (SMC) method....
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Living Reference Work Entry In depth
Deep Learning in a System Identification Perspective
The use of deep learning for sequence learning problems and system identification are intimately linked, and interesting opportunities exist on this cross section. The aim of this chapter is to briefly introdu...
-
Living Reference Work Entry In depth
Nonlinear System Identification Using Particle Filters
Particle filters are computational methods opening up for systematic inference in nonlinear/non-Gaussian state-space models. The particle filter constitute the most popular sequential Monte Carlo (SMC) method....
-
Reference Work Entry In depth
Nonlinear System Identification Using Particle Filters
Particle filters are computational methods opening up for systematic inference in nonlinear/non-Gaussian state-space models. The particle filters constitute the most popular sequential Monte Carlo (SMC) method...
-
Reference Work Entry In depth
Situational Awareness and Road Prediction for Trajectory Control Applications
Situational awareness is of paramount importance in all advanced driver assistance systems. Situational awareness can be split into the tasks of tracking moving vehicles and map** stationary objects in the i...