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
System identification of Wiener systems with B-spline functions using De Boor recursion
TL;DR: A simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data and the Gauss–Newton algorithm is combined with De Boor algorithm for the parameter estimation of theWiener model, together with the use of a parameter initialisation scheme.
Journal ArticleDOI
On a class of optimization-based robust estimators
TL;DR: This paper considers the problem of estimating a parameter matrix from observations which are affected by two types of noise components: a sparse noise sequence which, whenever nonzero can have arbitrarily large amplitude and a dense and bounded noise sequence of “moderate” amount.
Journal ArticleDOI
Concurrent processing of heteroskedastic vector-valued mixture density models
TL;DR: A combined two-stage least-squares (2SLS)–expectation maximization (EM) algorithm for estimating vector-valued autoregressive conditional heteroskedasticity models with standardized errors generated by Gaussian mixtures is introduced.
Posted Content
Event-triggered Learning for Linear Quadratic Control
TL;DR: A structured approach is obtained that decides when model learning is beneficial, by analyzing the probability distribution of the linear quadratic cost and designing a learning trigger that leverages Chernoff bounds.
Lokale Modellnetze zur Identifikation und Versuchsplanung nichtlinearer Systeme
TL;DR: In this paper, the authors propose new approaches for experimental, data-based modeling (identification) and for experimental design of nonlinear models based on local model networks, which can be found in the HILOMOT (Hierarchical Local Model Tree).