A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization

DOI10.1016/j.ins.2013.02.017zbMath1320.68151OpenAlexW1977149227MaRDI QIDQ497132

Rob Law, Qi Wu, Edmond Wu, Jinxing Lin

Publication date: 23 September 2015

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2013.02.017

zbMATH Keywords

forecastparticle swarm optimizationchaotic mappingGaussian loss functionsupport vector regression machine

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (5)

A comprehensive survey on particle swarm optimization algorithm and its applications ⋮ Point and interval forecasting for carbon price based on an improved analysis-forecast system ⋮ \(K\)-plane regression ⋮ Tuning kernel parameters for SVM based on expected square distance ratio ⋮ Online optimal decoupled sliding mode control based on moving least squares and particle swarm optimization

Uses Software

• Human Mortality

Cites Work

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• A parsimony fuzzy rule-based classifier using axiomatic fuzzy set theory and support vector machines
• Particle swarm optimization for determining fuzzy measures from data
• Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system
• Induction of multiple fuzzy decision trees based on rough set technique
• Regularized least squares support vector regression for the simultaneous learning of a function and its derivatives
• On-line fuzzy modeling via clustering and support vector machines
• Electric load forecasting by support vector model
• Fuzzy vector quantization with the particle swarm optimization: a study in fuzzy granulation-degranulation information processing
• A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation
• Logic-based neural networks
• An improved particle swarm optimization algorithm combined with piecewise linear chaotic map
• A \(\nu \)-twin support vector machine (\(\nu \)-TSVM) classifier and its geometric algorithms

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