Book ChapterDOI
System Identification I
Biao Huang,Yutong Qi,Akm Monjur Murshed +2 more
- pp 31-56
Reads0
Chats0
About:
The article was published on 2012-12-11. It has received 1704 citations till now. The article focuses on the topics: Nonlinear system identification & System identification.read more
Citations
More filters
Posted Content
Experimental analysis of data-driven control for a building heating system
TL;DR: Model-assisted batch reinforcement learning is applied to the setting of building climate control subjected to dynamic pricing and it is found that within 10 to 20 days sensible policies are obtained that can be used for different outside temperature regimes.
Journal ArticleDOI
Learning GP-BayesFilters via Gaussian process latent variable models
Jonathan Ko,Dieter Fox +1 more
TL;DR: GPBF-Learn is introduced, a framework for training GP-BayesFilters without ground truth states that extends Gaussian Process Latent Variable Models to the setting of dynamical robotics systems and shows how weak labels for the groundtruth states can be incorporated into the GPBF- learn framework.
Journal ArticleDOI
Robust Kalman filtering for nonlinear multivariable stochastic systems in the presence of non‐Gaussian noise
Vladimir Stojanovic,Novak Nedic +1 more
TL;DR: Improvement of performances and practical values of the Masreliez‐Martin filter as well as the tendency to expand its application to nonlinear systems represent motives to design the modified extended Mas Reliez–Martin filter.
Journal ArticleDOI
Linear parametric hydrodynamic models for ocean wave energy converters identified from numerical wave tank experiments
TL;DR: In this paper, a new modelling methodology was developed to combine the fidelity of CFD models with the computational attractiveness of BEM-type models, which can give representative linear models, or be extended into the nonlinear domain, as desired.
Journal ArticleDOI
A Multi-Patient Data-Driven Approach to Blood Glucose Prediction
Alessandro Aliberti,Irene Pupillo,Stefano Terna,Enrico Macii,Santa Di Cataldo,Edoardo Patti,Andrea Acquaviva +6 more
TL;DR: This paper investigates the prediction models trained on glucose signals of a large and heterogeneous cohort of patients and then applied to infer future glucose-level values on a completely new patient, designed and compared two different types of solutions that were proved successful in many time-series prediction problems based respectively, on non-linear autoregressive (NAR) neural network and on long short-term memory (LSTM) networks.
References
More filters
Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Journal ArticleDOI
Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
Milan Korda,Igor Mezic +1 more
TL;DR: This work extends the Koopman operator to controlled dynamical systems and applies the Extended Dynamic Mode Decomposition (EDMD) to compute a finite-dimensional approximation of the operator in such a way that this approximation has the form of a linearcontrolled dynamical system.
Journal ArticleDOI
A Tour of Reinforcement Learning: The View from Continuous Control
TL;DR: The authors surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications, and reviews the general formulation, terminology, and techniques for reinforcement learning for continuous control.
Journal ArticleDOI
SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
Petre Stoica,Prabhu Babu,Jian Li +2 more
TL;DR: This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing, obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many- snapshot cases but can be used even in single-snapshot situations.