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
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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.
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Convex vs nonconvex approaches for sparse estimation: GLasso, Multiple Kernel Learning and Hyperparameter GLasso
TL;DR: In this paper, a non-convex estimator based on the Group Lasso approach is proposed for sparse estimation with a group of variables, where the underlying optimization problem is not convex.
Journal ArticleDOI
Modeling and identification of uncertain-input systems
TL;DR: In this article, uncertain-input models are used to encode prior information about the input or the linear system, and an approximation approach based on variational Bayes is developed to find the hyperparameters that rely on the EM method and results in decoupled update steps.
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TL;DR: System identification deals with the estimation of mathematical models from experimental data as mathematical models are built for specific purposes, ensuring that the estimated model represents t as mentioned in this paper, which is a special case of system identification.
Book ChapterDOI
Automatic Balancing of Rotor-Bearing Systems
Andrés Blanco-Ortega,Gerardo Silva-Navarro,Jorge Colín-Ocampo,Marco Antonio Oliver-Salazar,Gerardo Vela-Valdés +4 more
TL;DR: Control of machinery vibration is essential in the industry today to increase running speeds and the requirement for rotating machinery to operate within specified levels of vibration.
Proceedings ArticleDOI
Time-triggered control of nonlinear discrete-time systems
Romain Postoyan,Dragan Nesic +1 more
TL;DR: The time-triggered control of nonlinear discrete-time systems using an emulation approach and provides conditions to preserve stability when the control input is no longer updated at each step, but within N steps from the previous update, where N is a strictly positive integer.