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
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
Localization of Near-Field Sources Based on Linear Prediction and Oblique Projection Operator
TL;DR: An oblique projection based alternating iterative scheme is presented to improve the accuracy of the estimated location parameters and shows that the LPATS provides good estimation performance for both the DOAs and ranges compared to some existing methods.
Posted Content
Identification of parameterized gray-box state-space systems: from a black-box linear time-invariant representation to a structured one: detailed derivation of the gradients involved in the cost functions.
TL;DR: In this paper, the problem of providing a reliable initial vector of parameters is tackled by assuming that a reliable initialized state-space model of the system is available, and an algorithm dedicated to non-convex optimization is presented in order to transform the initial fully-parameterized model into the structured state space parameterization satisfied by the system to be identified.
Journal ArticleDOI
Nonparametric Data-Driven Modeling of Linear Systems: Estimating the Frequency Response and Impulse Response Function
TL;DR: Combining periodic signals with the advanced methods presented in this article provides access to highquality FRF measurements, while the measurement time is reduced by eliminating disturbing transient effects.
Proceedings ArticleDOI
Recurrent Neural Network based MPC for Process Industries
Nicolas Lanzetti,Yingzhao Lian,Andrea Cortinovis,Luis Dominguez,Mehmet Mercangöz,Colin N. Jones +5 more
TL;DR: This article combines data-driven modeling with MPC and investigates how to train, validate, and incorporate a special recurrent neural network (RNN) architecture into an MPC framework, designed for being scalable and applicable to a wide range of multiple-input multiple-output systems encountered in industrial applications.
Proceedings ArticleDOI
Cyberphysical-system-on-chip (CPSoC): a self-aware MPSoC paradigm with cross-layer virtual sensing and actuation
TL;DR: CPSoC is proposed, a new class of sensor and actuator-rich multiprocessor systems-on-chip (MPSoCs), that augment MPSoCs with additional on-chip and cross-layer sensing and actuation capabilities to enable self-awareness within the observe-decide-act (ODA) paradigm.