LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting

LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting

The importance of optimizing Least Squares Support Vector Machines (LSSVM) embedded control parameters has motivated researchers to search for proficient optimization techniques. In this study, a new metaheuristic algorithm, viz., Grey Wolf Optimizer (GWO), is employed to optimize the parameter...

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Bibliographic Details
Main Authors: Zuriani, Mustaffa, Mohd Herwan, Sulaiman, M. N. M., Kahar
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/11215/
http://umpir.ump.edu.my/id/eprint/11215/
http://umpir.ump.edu.my/id/eprint/11215/1/LS-SVM%20Hyper-parameters%20Optimization%20based%20on%20GWO%20Algorithm%20for%20Time%20Series%20Forecasting.pdf

Internet

http://umpir.ump.edu.my/id/eprint/11215/
http://umpir.ump.edu.my/id/eprint/11215/
http://umpir.ump.edu.my/id/eprint/11215/1/LS-SVM%20Hyper-parameters%20Optimization%20based%20on%20GWO%20Algorithm%20for%20Time%20Series%20Forecasting.pdf

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