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
Reconstruction of Precordial Lead Electrocardiogram From Limb Leads Using the State-Space Model
TL;DR: It was found that the reconstruction performance depended on the type of disease rather than lead type, and when ECG contaminated with the noise was used for reconstruction, the proposed method demonstrated better performance than linear regression model in general.
Proceedings ArticleDOI
Sign-Perturbed Sums (SPS) with instrumental variables for the identification of ARX systems
TL;DR: The proposed construction is based on the instrumental variables estimate and, unlike the original SPS, it can construct non-asymptotic confidence regions for linear regression models where the regressors contain past values of the output.
Journal ArticleDOI
Modeling, design, and flight testing of three flutter controllers for a flying-wing drone
David K. Schmidt,Brian P. Danowsky,Aditya Kotikalpudi,Julian Theis,Christopher D. Regan,Peter Seiler,Rakesh K. Kapania +6 more
TL;DR: Three flutter-suppression designs for a flexible flying-wing research drone are discussed, along with the modeling and flight-test results.
Identification for control of complex motion systems : optimal numerical conditioning using data-dependent polynomial bases
TL;DR: The final author version and the galley proof are versions of the publication after peer review that features the final layout of the paper including the volume, issue and page numbers.
Proceedings ArticleDOI
KAPow: A System Identification Approach to Online Per-Module Power Estimation in FPGA Designs
Eddie Hung,James J. Davis,Joshua M. Levine,Edward Stott,Peter Y. K. Cheung,George A. Constantinides +5 more
TL;DR: This work combines board-level power measurements with register-level activity counting to build an online model that produces a breakdown of power consumption within the design, and shows it to be accurate, with per-module power estimates as close to ±5mW of true measurements, and to have low overheads.