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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Proceedings ArticleDOI
Experimental study of economic model predictive control in building energy systems
TL;DR: This paper presents results from testing an economic model predictive control strategy in an office building located in Milwaukee, Wisconsin, USA, that was successful at reducing energy costs compared to the baseline case for the considered building.
Journal ArticleDOI
A unified SVM framework for signal estimation
TL;DR: In this paper, the authors present a unified framework for tackling estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs), which can be designed to take into account different noise sources in the formulation and to fuse heterogeneous information sources.
Journal ArticleDOI
A methodology for identification and control of electro-mechanical actuators
Tarek A. Tutunji,Ashraf Saleem +1 more
TL;DR: In this article, a three-stage methodology for real-time identification and control of electro-mechanical actuator plants is presented, tested, and validated, and the designed controller is applied and tested on the real plant through Hardware-in-the-Loop (HIL) environment.
Journal ArticleDOI
KAPow: High-Accuracy, Low-Overhead Online Per-Module Power Estimation for FPGA Designs
James J. Davis,Eddie Hung,Joshua M. Levine,Edward Stott,Peter Y. K. Cheung,George A. Constantinides +5 more
TL;DR: This work combines measurements of register-level switching activity and system-level power to build an adaptive online model that produces live breakdowns of power consumption within the design and proposes a strategy allowing for the identification and subsequent elimination of counters found to be of low significance at runtime, reducing algorithmic complexity without sacrificing significant accuracy.
Posted Content
Feedback Linearization based on Gaussian Processes with event-triggered Online Learning
Jonas Umlauft,Sandra Hirche +1 more
TL;DR: In this article, the authors proposed a learning feedback linearizing control law using online closed-loop identification, which ensures high data efficiency and reduces the computational complexity of Gaussian processes under real-time constraints.
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.