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
ALVEN: Algebraic learning via elastic net for static and dynamic nonlinear model identification
Weike Sun,Richard D. Braatz +1 more
TL;DR: An algorithm is proposed that combines nonlinear feature generation and sparse regression to learn interpretable nonlinear models from noisy and limited data, called ALVEN, to produce an interpretable model useful for process applications while avoiding overfitting.
Proceedings ArticleDOI
Model reduction by moment matching for linear switched systems
TL;DR: A moment-matching method for the model reduction of linear switched systems (LSSs) based upon a partial realization theory of LSSs and it is similar to the Krylov subspace methods used for moment matching for linear systems.
Journal ArticleDOI
Revealing the information contents of memory within the stimulus information representation framework
TL;DR: It is illustrated how SIR can reverse engineer the information contents of memory from behavioural and brain measures in the context of specific cognitive tasks that involve memory, and discusses two specific lessons from this approach that generally apply to memory studies.
Journal ArticleDOI
Maximum likelihood estimation of linear SISO models subject to missing output data and missing input data
Ragnar Wallin,Anders Hansson +1 more
TL;DR: An approach to maximum likelihood estimation of linear single input single output (SISO) models when both input and output data are missing that is based on the Euclidean norm of the prediction error vector scaled by a particular function of the covariance matrix of the observed output data.
Journal ArticleDOI
Learning-based predictive control of the cooling system of a large business centre
TL;DR: The design of an advanced control algorithm for the cooling system of a large business and commercial centre, which comprises phenomena that are difficult to model with physical principles, is described, showing significant potential improvements with respect to the baseline controller currently employed.