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.
References
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Journal ArticleDOI
Learning control in robot-assisted rehabilitation of motor skills – a review
TL;DR: Experimental studies of human motor learning, in robotically controlled environments, indicate that a model consisting of a classical (iterative) learning control augmented with an appropriate kinematic model ofhuman motor motion fits the observed human learning behaviour well.
Journal ArticleDOI
Identification of Wave Energy Device Models From Numerical Wave Tank Data—Part 2: Data-Based Model Determination
TL;DR: In this paper, the identification of mathematical models describing the behavior of wave energy devices (WECs) in the ocean is investigated through the use of numerical wave tank experiments, and the authors propose to use discrete-time nonlinear autoregressive with exogenous input (NARX) models, as an alternative to continuous-time models.
Proceedings ArticleDOI
Estimation of human ankle impedance during walking using the perturberator robot
TL;DR: A novel method for removing the angle and torque profiles that resulted from walking was introduced that uses bootstrapping while taking the subtraction of multiple averaged perturbed and non-perturbed trials.
Journal ArticleDOI
Health Monitoring of Civil Infrastructures by Subspace System Identification Method: An Overview
TL;DR: This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures, and considers the subspace algorithm to resolve the problem of a real-world application for SHM.
Journal ArticleDOI
Sparse identification of nonlinear dynamical systems via reweighted ℓ1-regularized least squares
TL;DR: The aim of this work is to improve the accuracy and robustness of SINDy in the presence of state measurement noise, and a reweighted l 1 -regularized least squares solver is developed, wherein the regularization parameter is selected from the corner point of a Pareto curve.