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
Citations
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Journal ArticleDOI
Quantifying ventilatory control stability from spontaneous sigh responses during sleep: a comparison of two approaches.
Leonardo Nava-Guerra,Bradley A. Edwards,Philip I. Terrill,Scott A. Sands,Raouf S. Amin,James S. Kemp,Michael C. K. Khoo +6 more
TL;DR: The agreement found between the two mathematical models indicates that either methodology can be used indistinctively providing reliable results and their application can expand to sigh data from other clinical cohorts of preterm infants.
Journal ArticleDOI
Interpretable machine learning methods for predictions in systems biology from omics data
TL;DR: This work introduces views from the interpretable machine learning community and proposes a scheme for categorizing studies on omics data, and tries to answer the question: “What is interpretability?”
Journal ArticleDOI
Separating regressions for model fitting to reduce the uncertainty in forest volume-biomass relationship.
TL;DR: In this paper, the SABB equation is re-expressed by two Parametric Equations (PEs) for separating regressions, and a graphical analysis of the PEs proposes a concept of restricted zone, which helps to diagnose parameters of the volume-SABB equations in regression analyses of field data.
Journal ArticleDOI
From Linear to Nonlinear Model Predictive Control of a Building
TL;DR: In this article, the gap between LMPC and NMPC is bridged by introducing several variants of linear time-varying model predictive controller (LTVMPC), which obtains predictions which are closer to reality than those of the linear time invariant model while still keeping the optimization task convex.
Journal ArticleDOI
Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities
David Lao-Martil,K. J. Verhagen,Joep P. J. Schmitz,Bas Teusink,S. Aljoscha Wahl,Natal A. W. van Riel +5 more
TL;DR: This review systematically evaluates the literature to describe the current advances, limitations, and opportunities in central carbon metabolism for Saccharomyces cerevisiae and concludes that no commonly applicable model has been presented.
References
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Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
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
A Tour of Reinforcement Learning: The View from Continuous Control
TL;DR: The authors surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications, and reviews the general formulation, terminology, and techniques for reinforcement learning for continuous control.
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.