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
Friction Effect Analysis of a DC Motor
TL;DR: In this article, the authors deal with experimental method of DC motor friction identification and determine the suitable way of friction modeling for DC motor by means of experimental method done using Newton's mechanics.
Journal ArticleDOI
Characterization of Driver Neuromuscular Dynamics for Human–Automation Collaboration Design of Automated Vehicles
Chen Lv,Huaji Wang,Dongpu Cao,Yifan Zhao,Daniel J. Auger,Mark J.M. Sullman,Rebecca Matthias,Lee Skrypchuk,Alexandros Mouzakitis +8 more
TL;DR: A dynamic model of drivers’ neuromuscular interaction with a steering wheel is first established and key parameters of the transfer function model are identified by using the Gauss–Newton algorithm.
Proceedings Article
Optimal Control Via Neural Networks: A Convex Approach
TL;DR: In this paper, the authors design input convex recurrent neural networks to capture temporal behavior of dynamical systems, and then optimal controllers can be achieved via solving a convex model predictive control problem.
Journal ArticleDOI
Errors-in-variables identification in dynamic networks - Consistency results for an instrumental variable approach
TL;DR: The identification of a linear module that is embedded in a dynamic network using noisy measurements of the internal variables of the network is considered, and a flexible choice of which internal variables need to be measured in order to identify the module of interest is considered.
Journal ArticleDOI
Engine idle-speed system modelling and control optimization using artificial intelligence
TL;DR: A novel modelling and optimization approach for steady state and transient performance tune-up of an engine at idle speed and results indicate that PSO is more efficient than the GA in an idle-speed control optimization problem based on the LS-SVM model can be applied to different engine modelling and control optimization problems.