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
Sensor Placement Strategy for Pipeline Condition Assessment Using Inverse Transient Analysis
TL;DR: This paper investigates how the number and location of pressure sensors affects the identifiability of pipeline parameters in the ITA approach, and finds that at least three sensors are needed.
Journal ArticleDOI
A control-theoretic approach towards joint admission control and resource allocation of cloud computing services
Dimitrios Dechouniotis,Nikolaos Leontiou,Nikolaos Athanasopoulos,Athanasios Christakidis,Spyros Denazis +4 more
TL;DR: This article addresses the admission control and resource allocation problem jointly, by establishing a unified modeling and control framework and ensures convergence to a desired reference point and stability and feasibility of the control strategy are guaranteed while achieving high performance of the co-hosted web applications.
Journal ArticleDOI
Reliable Online Parameter Identification of Li-Ion Batteries in Battery Management Systems Using the Condition Number of the Error Covariance Matrix
TL;DR: It is shown with a1-RC equivalent circuit model that the proposed CNRLS algorithm is more noise-tolerant and accurate than two benchmarks including the standard RLS and adaptive forgetting factor RLS (AFFRLS) in terms of mean absolute errors, with almost the same computing cost.
Proceedings ArticleDOI
Dual control approach for zone model predictive control
TL;DR: A dual control algorithm based on the maximization of the smallest eigenvalue of the information matrix increase ensuring both the appropriately informative data and satisfaction of the control performance is presented.
Proceedings ArticleDOI
Convex vs nonconvex approaches for sparse estimation: Lasso, Multiple Kernel Learning and Hyperparameter Lasso
TL;DR: An approach alternative to Group Lasso is derived, also providing its connection with Multiple Kernel Learning and showing that the new technique obtains sparse solutions more accurate than the other two convex estimators.
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.