Optimal control by least squares support vector machines

By Johan A. K. Suykens, Joos Vandewalle, B. De Moor. Neural Networks, 2001

539 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8e561ff532f46441c6037b1f6381bd7c24e03e9a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

Citations in the corpus

Application of Hyperspectral Imaging to Discriminate the Variety of Maize Seeds (2015, methodology): “SVM is a classical nonlinear modeling method and is widely applied in pattern recognition and function estimation (Suykens et al. 2001).”
Time series filtering, smoothing and learning using the kernel Kalman filter (2005, methodology): “Two other works are closely related to ours but are limited to the nonlinear filtering problem: the approach described in [11], which tackles the so-called N-stage optimal control problem using support vector regressors to model the transition process and the work developed in [12], which proposes t”
Weighted least squares support vector machines: robustness and sparse approximation (2002, background): “In the latter case convexity is lost, but some of the other interesting SVM properties remain applicable.”
Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats (2015, unknown): “The detailed implementation steps of PLSDA and RBF-SVM were reported in previous studies (Menesatti et al. 2009; Suykens et al., 2001).”
Review on Studies and Advances of Machine Learning Approaches (2013, background): “NNs has made successful applications in many fields [5-7].”
Volatility forecasting of crude oil market: A new hybrid method (2018, background): “Specifically, based on the original forecastingmodel of crude oil price volatility, we also bring the least squares support vector machine (LSSVM) model to forecast the residual series because of its superior nonlinear modeling capacity (Suykens & Vandewalle, 1999; Suykens et al., 2001; Yuan & Lee, ”
Financial time series prediction using least squares support vector machines within the evidence framework (2001, background): “tions of LS-SVMs have allowed to make extensions toward recurrent neural networks [28] and nonlinear optimal control [29].”
Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis (2002, background): “The primal-dual interpretations and equality constraints of LS-SVMs have also allowed to extend the LS-SVM framework to recurrent networks and optimal control (Suykens & Vandewalle, 2000; Suykens et al., 2001).”
Enhancing Visible and Near-Infrared Hyperspectral Imaging Prediction of TVB-N Level for Fish Fillet Freshness Evaluation by Filtering Optimal Variables (2017, background): “…with a least squares cost function, and utilizes nonlinear map function and projects into features to a high-dimensional space and adopts the Lagrange multiplier to calculate the partial differentiation of each feature to attain the optimal resolution (Cawley and Talbot 2002; Suykens et al. 2001).”
Benchmarking Least Squares Support Vector Machine Classifiers (2004, methodology): “The primal-dual formulations of the SVM framework and the equality constraints of the LS-SVM formulation allow to make extensions towards recurrent neural networks (Suykens & Vandewalle, 2000) and nonlinear optimal control ( Suykens, Vandewalle, & De Moor, 2001 ).”
Time Series Prediction Using Support Vector Machines: A Survey (2009, background): “[78] provided a detailed summary with real world (simplified) examples of non-linear control system theory”