Radial basis function neural network learning with modified backpropagation algorithm
Radial Basis Function Neural Network (RBFNN) is a class of Artificial Neural Network (ANN) widely used in science and engineering for classification problems with Backpropagation (BP) algorithm. However, major disadvantages of BP are due to the relatively slow convergence rate and always being trapp...
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| 第一著者: | |
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| フォーマット: | 学位論文 |
| 言語: | English |
| 出版事項: |
2014
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| 主題: | |
| オンライン･アクセス: | http://eprints.utm.my/id/eprint/48593/1/UsmanMuhammadTukurMFC2014.pdf http://eprints.utm.my/id/eprint/48593/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85206?queryType=vitalDismax&query=Radial+basis+function+neural+network+learning+with+modified+backpropagation+algorithm&public=true |
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http://eprints.utm.my/id/eprint/48593/1/UsmanMuhammadTukurMFC2014.pdfhttp://eprints.utm.my/id/eprint/48593/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85206?queryType=vitalDismax&query=Radial+basis+function+neural+network+learning+with+modified+backpropagation+algorithm&public=true