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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Systems Theory for Pharmaceutical Drug Discovery
TL;DR: The ultimate goal of the work presented in this thesis is to create a framework which can be used to rationally select new drug targets and also be able to create personalized medicine treatments which are tailored to the particular phenotypic behavior of an individual's disease.
Journal ArticleDOI
Optimizing a DIscrete Loss (ODIL) to solve forward and inverse problems for partial differential equations using machine learning tools
TL;DR: The Optimizing a Discrete Loss (ODIL) framework for the numerical solution of Partial Differential Equations (PDE) is introduced using machine learning tools and the value of this approach on equations that may have missing parameters or where no sufficient data is available to form a well-posed initial-value problem is demonstrated.
Experiment design for identification of structured linear systems
TL;DR: This thesis introduces Stealth and Sensitivity methods that enable the applicability of the LCED framework to structured and unstructured systems regulated by unknown or nonlinear controllers and introduces the novel Minimum Experiment Time algorithm, which solves an optimisation problem formulated by Ebadat et al. (2014b).
Proceedings ArticleDOI
Market-based coordination of thermostatically controlled loads-Part II: Unknown parameters and case studies
TL;DR: In this paper, the authors considered the coordination of a population of thermostatically controlled loads with unknown parameters to achieve group objectives and proposed a joint state and parameter estimation framework based on the expectation maximization algorithm.
Proceedings ArticleDOI
System Identification using LMS, RLS, EKF and Neural Network
TL;DR: Different types of system identification techniques were used and EKF provided the best performance in terms of parameter accuracy and convergence rate in the DC- motor use-case.
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.