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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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
Continuous-time linear time-varying system identification with a frequency-domain kernel-based estimator
TL;DR: In this paper, a kernel-based estimator for the identification of continuous-time linear time-varying systems is presented. But the estimator is not suitable for noisy signals and the model complexity is formulated as an optimization problem with continuous variables.
Proceedings ArticleDOI
An LSTM based classification method for time series trend forecasting
TL;DR: The LSTM model is implemented, a special recurrent neural network that learns long term dependencies that is suitable for predicting time series with both long term and short term dependencies, and the results outperform that of the conventional auto regression models.
Journal ArticleDOI
Nonlinear predictive model selection and model averaging using information criteria
TL;DR: Three commonly used criteria, namely, Akaike information criterion, Bayesian information criterion and an adjustable prediction error sum of squares (APRESS) are investigated and their performance in model selection and model averaging is evaluated via a number of case studies using both simulation and real data.
Journal ArticleDOI
Time-variant modelling of heart rate responses to exercise intensity during road cycling
TL;DR: In this article, the heart rate responses to training intensity during road cycling were modelled with compact time-variant mathematical model structures, and the model performance was evaluated in terms of model order (complexity), number of inputs and parameter estimation methods used (time-invariant vs. time-varying).
Journal ArticleDOI
Soft Sensor Transferability: A Survey
TL;DR: The proposed survey reports the state of the art of TL techniques for nonlinear dynamical SSs design and discusses methods and applications used for transfer learning in soft Sensors.