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Integrated neural network model with pre-RBF kernels
| Content Provider | SAGE Publishing |
|---|---|
| Author | Wen, Hui Yan, Tao Liu, Zhiqiang Chen, Deli |
| Copyright Year | 2021 |
| Abstract | To improve the network performance of radial basis function (RBF) and back-propagation (BP) networks on complex nonlinear problems, an integrated neural network model with pre-RBF kernels is proposed. The proposed method is based on the framework of a single optimized BP network and an RBF network. By integrating and connecting the RBF kernel mapping layer and BP neural network, the local features of a sample set can be effectively extracted to improve separability; subsequently, the connected BP network can be used to perform learning and classification in the kernel space. Experiments on an artificial dataset and three benchmark datasets show that the proposed model combines the advantages of RBF and BP networks, as well as improves the performances of the two networks. Finally, the effectiveness of the proposed method is verified. |
| Related Links | https://journals.sagepub.com/doi/pdf/10.1177/00368504211026111?download=true |
| ISSN | 00368504 |
| Issue Number | 3 |
| Volume Number | 104 |
| Journal | Science Progress (SCI) |
| e-ISSN | 20477163 |
| DOI | 10.1177/00368504211026111 |
| Language | English |
| Publisher | Sage Publications UK |
| Publisher Date | 2021-08-06 |
| Publisher Place | London |
| Access Restriction | Open |
| Rights Holder | © The Author(s) 2021 |
| Subject Keyword | back propagation radial basis function Neural network network integration kernel mapping |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
