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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Proceedings ArticleDOI
Identifying parameters in active magnetic bearing system using LFT formulation and Youla factorization
TL;DR: The main focus of the paper relies on how to effectively identify uncertain parameters, such as stiffness and damping force coefficients of bearings and seals in rotordynamic systems.
Journal ArticleDOI
Dynamic Neural Network-Based Adaptive Tracking Control for an Autonomous Underwater Vehicle Subject to Modeling and Parametric Uncertainties
Filiberto Munoz,Jorge S. Cervantes-Rojas,Jose M. Valdovinos,Omar Sandre-Hernandez,Sergio Salazar,Hugo Romero +5 more
TL;DR: An identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) is proposed as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain.
Posted Content
Sample Complexity of Sparse System Identification Problem
Salar Fattahi,Somayeh Sojoudi +1 more
TL;DR: A sparsity promoting block-regularized estimator to identify the dynamics of the system with only a limited number of input-state data samples is proposed, and it is shown that this estimator results in a small element-wise error, provided that the number of sample trajectories is above a threshold.
Journal ArticleDOI
Heavy vehicle suspension parameters identification and estimation of vertical forces: experimental results
Hocine Imine,T. Madani +1 more
TL;DR: Suspension stiffness and unsprung masses have been identified and Experimental results carried out on an instrumented tractor have been presented in order to show the quality of the state observation, parameters identification and force estimation.
Scramjet isolator modeling and control
TL;DR: In this article, the authors used shadowgraph images to measure the shock train leading edge (LE) position with root mean square (RMS) errors less than 20% of a duct height.