Simple Case Study on Radius of Radial Basis Function Network for Sequential Approximate Optimization
Radial basis function (RBF) networks are used for various research field. Especially, they make handling easy for classification and function approximation due to their mathematical form. Some parameters of an RBF network should be carefully selected to obtain good performance for a specific problem...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-11-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
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| Online Access: | http://dx.doi.org/10.9746/jcmsi.10.551 |
| Summary: | Radial basis function (RBF) networks are used for various research field. Especially, they make handling easy for classification and function approximation due to their mathematical form. Some parameters of an RBF network should be carefully selected to obtain good performance for a specific problem. This study investigates the radius of an RBF network in a simple case for sequential approximate optimization. The result shows that there is an effective radius range for sequential approximate optimization methods. |
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| ISSN: | 1884-9970 |