Parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function

DOI10.1155/2017/3614790zbMath1400.93305OpenAlexW2766177754MaRDI QIDQ1794163

Hailun Wang, Daxing Xu

Publication date: 15 October 2018

Published in: Journal of Control Science and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2017/3614790

zbMATH Keywords

nonlinear systemstate estimationKalman filtersupport vector regressionadaptive fusionmixed kernel function

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Learning and adaptive systems in artificial intelligence (68T05) Estimation and detection in stochastic control theory (93E10)

Uses Software

• LIBSVM

Cites Work

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• A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection
• Stochastic gradient descent with Barzilai-Borwein update step for SVM
• Support-vector networks
• High-degree cubature Kalman filter
• Lower Bounds on the Size of Semidefinite Programming Relaxations
• Bayesian Support Vector Regression With Automatic Relevance Determination Kernel for Modeling of Antenna Input Characteristics

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