Book ChapterDOI
System Identification I
Biao Huang,Yutong Qi,Akm Monjur Murshed +2 more
- pp 31-56
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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
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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
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.
Journal ArticleDOI
A new kernel-based approach for linear system identification
TL;DR: A new kernel-based approach for linear system identification of stable systems that model the impulse response as the realization of a Gaussian process whose statistics include information not only on smoothness but also on BIBO-stability.
Journal ArticleDOI
Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping
TL;DR: The results demonstrate that the six-degree-of-freedom trajectory of a passive spring-mounted range sensor can be accurately estimated from laser range data and industrial-grade inertial measurements in real time and that a quality 3-D point cloud map can be generated concurrently using the same data.
References
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Journal ArticleDOI
Machine learning for fast and reliable solution of time-dependent differential equations
TL;DR: It is proved that ANN models are able to approximate every time-dependent model described by ODEs with any desired level of accuracy, and is tested on different problems, including the model reduction of two large-scale models.
Journal ArticleDOI
Imitation learning for agile autonomous driving
Yunpeng Pan,Ching-An Cheng,Kamil Saigol,Keuntaek Lee,Xinyan Yan,Evangelos A. Theodorou,Byron Boots +6 more
TL;DR: This work presents an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost on-board sensors and shows that policies trained with online imitation learning overcome well-known challenges related to covariate shift and generalize better than policiestrained with batch imitation learning.
Journal ArticleDOI
A shift in paradigm for system identification
TL;DR: The purpose of this contribution is to provide an accessible account of the main ideas and results of kernel-based regularisation methods for system identification.
Journal ArticleDOI
Refined instrumental variable estimation
TL;DR: The paper shows that, contrary to apparently widely held beliefs, the iterative RIV algorithm provides a reliable solution to the maximum likelihood optimization equations for this class of Box-Jenkins transfer function models and so its en bloc or recursive parameter estimates are optimal in maximum likelihood, prediction error minimization and instrumental variable terms.
Proceedings ArticleDOI
Linear Model Predictive Safety Certification for Learning-Based Control
TL;DR: In this article, a model predictive safety certification (MPSC) scheme for linear systems with additive disturbances is proposed, which verifies safety of a proposed learning-based input and modifies it as little as necessary in order to keep the system within a given set of constraints.