System identification: A machine learning perspective
A Chiuso, G Pillonetto - Annual Review of Control, Robotics, and …, 2019 - annualreviews.org
Estimation of functions from sparse and noisy data is a central theme in machine learning. In
the last few years, many algorithms have been developed that exploit Tikhonov …
the last few years, many algorithms have been developed that exploit Tikhonov …
A shift in paradigm for system identification
System identification is a mature research area with well established paradigms, mostly
based on classical statistical methods. Recently, there has been considerable interest in so …
based on classical statistical methods. Recently, there has been considerable interest in so …
Kernel methods and gaussian processes for system identification and control: A road map on regularized kernel-based learning for control
The commonly adopted route to control a dynamic system and make it follow the desired
behavior consists of two steps. First, a model of the system is learned from input–output data …
behavior consists of two steps. First, a model of the system is learned from input–output data …
On asymptotic properties of hyperparameter estimators for kernel-based regularization methods
The kernel-based regularization method has two core issues: kernel design and
hyperparameter estimation. In this paper, we focus on the second issue and study the …
hyperparameter estimation. In this paper, we focus on the second issue and study the …
Nonlinear industrial soft sensor development based on semi-supervised probabilistic mixture of extreme learning machines
Soft sensors play an important role in process industries for monitoring and control of key
quality variables, and calibration of analyzers. Owing to the merits of fast learning speed and …
quality variables, and calibration of analyzers. Owing to the merits of fast learning speed and …
[HTML][HTML] The existence and uniqueness of solutions for kernel-based system identification
M Khosravi, RS Smith - Automatica, 2023 - Elsevier
The notion of reproducing kernel Hilbert space (RKHS) has emerged in system identification
during the past decade. In the resulting framework, the impulse response estimation …
during the past decade. In the resulting framework, the impulse response estimation …
Hyperspectral image classification based on parameter-optimized 3D-CNNs combined with transfer learning and virtual samples
X Liu, Q Sun, Y Meng, M Fu, S Bourennane - Remote Sensing, 2018 - mdpi.com
Recent research has shown that spatial-spectral information can help to improve the
classification of hyperspectral images (HSIs). Therefore, three-dimensional convolutional …
classification of hyperspectral images (HSIs). Therefore, three-dimensional convolutional …
A new dataset and performance evaluation of a region-based cnn for urban object detection
A Dominguez-Sanchez, M Cazorla, S Orts-Escolano - Electronics, 2018 - mdpi.com
In recent years, we have seen a large growth in the number of applications which use deep
learning-based object detectors. Autonomous driving assistance systems (ADAS) are one of …
learning-based object detectors. Autonomous driving assistance systems (ADAS) are one of …
On semiseparable kernels and efficient implementation for regularized system identification and function estimation
T Chen, MS Andersen - Automatica, 2021 - Elsevier
A long-standing problem for kernel-based regularization methods is their high computational
complexity O (N 3), where N is the number of data points. In this paper, we make a …
complexity O (N 3), where N is the number of data points. In this paper, we make a …
Online glucose prediction using computationally efficient sparse kernel filtering algorithms in type-1 diabetes
Streaming data from continuous glucose monitoring (CGM) systems enable the recursive
identification of models to improve estimation accuracy for effective predictive glycemic …
identification of models to improve estimation accuracy for effective predictive glycemic …