Book ChapterDOI
System Identification I
Biao Huang,Yutong Qi,Akm Monjur Murshed +2 more
- pp 31-56
About:
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
Robot drives diagnostics by identifiability criterion based on state matrix
TL;DR: The identifiability conditions are considered not only in relation to the rank of the extended matrix [C, AC, ...], but also as a condition for ensuring the accuracy of the model with respect to the object.
Dissertation
Reduction of coupled field models for the simulation of electrical machines and power electronic modules
TL;DR: In this paper, the authors developed an identification method aimed at producing reduced thermal models to estimate the thermal behavior of power electronic modules, which was validated on an industrial application dealing with a thermally coupled solid/fluid problem mainly governed by forced convection.
Journal ArticleDOI
Multisensor Parallel Largest Ellipsoid Distributed Data Fusion with Unknown Cross-Covariances.
TL;DR: The proposed parallel fusion structure is proposed to introduce the LE algorithm into a multisensor system with unknown cross-covariances, and three parallel fusion structures based on different estimate pairing methods are presented and analyzed.
Journal ArticleDOI
Intelligent control of HVAC systems. Part I: Modeling and synthesis
TL;DR: The study is performed from the perspective of giving a unitary control method to ensure high energy efficiency and air quality improving and construction of the mathematical model is performed only with a view to obtain a framework of HVAC intelligent control validation by numerical simulations.
Posted Content
Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples
TL;DR: This paper introduces an $\ell_1$-regularized estimation method that can accurately estimate the Markov parameters of the system, provided that the number of samples scale logarithmically with the system dimension, and improves the sample complexity of learning partially observed linear dynamical systems.