| Author: | Cho, Kin-wai |
| Title: | Using genetic algorithms for searching radial basis function centers |
| Degree: | M.Sc. |
| Year: | 1997 |
| Subject: | Neural networks (Computer science) Genetic algorithms Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies |
| Pages: | 63 leaves : ill. ; 30 cm |
| Language: | English |
| Abstract: | This dissertation proposes to use genetic algorithms to search for the function centers of radial basis function networks. The basis function centers are directly encoded in binary genetic strings. Non-locally tuned centers are found by a genetic algorithm and its performance is evaluated by several experiments such as XOR, overlapped Gaussian distribution clusters and non-overlapped Gaussian distribution clusters. A comparison between the standard RBF using K-means clustering and GA's is performed. Their mean squared error, training time, centers locations and decision surface are studied. The results show that the RBF networks produced by the genetic algorithm give smaller mean squared error, but one of the features of RBF networks is lost. The outputs of the RBF networks trained with the GA may be high in the regions not specified by the training data. This is because non-locally tuned centers are used. In our implementation, the training time is approximately 30 times longer than that of the K-means clustering. A linearly relationship between training time and the number of generations is also observed. It is concluded that the RBF networks produced by the GA is able to achieve good performance (in terms of mean squared error) if sufficient training data is available. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b12505213.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.11 MB | Adobe PDF | View/Open |
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