| Status | 已發表Published |
| Functional approximation using regularized orthogonal functional basis neural network | |
| Chen C.L.Philip; Cao Y.; LeClair Steven R. | |
| 1997-12-01 | |
| Source Publication | Intelligent Engineering Systems Through Artificial Neural Networks
 |
| Volume | 7 |
| Pages | 11-16 |
| Abstract | Subset selection [1-4] is a well-known technique for generating an efficient and effective neural network structure. The technique has been combined with regularization to improve the generalization performance of a neural network. In this paper, we show an incongruity involving subset selection and regularization. We present an approach to solve this dissonance wherein our subset selection is derived from a combination of functional basis. A more efficient training convergence speed is shown using the new basis which is derived from an 'orthogonal-functional-basis' transformation. With this transformation we propose a new Orthogonal Functional Basis Neural Network structure which is not only more computationally tractable but also gives better generalization performance. Simulation studies are presented that demonstrate the performance, behavior, and advantages of the proposed network. |
| URL | View the original |
| Language | 英語English |
| Fulltext Access | |
| Document Type | Conference paper |
| Collection | University of Macau |
| Affiliation | Wright State University |
| Recommended Citation GB/T 7714 | Chen C.L.Philip,Cao Y.,LeClair Steven R.. Functional approximation using regularized orthogonal functional basis neural network[C], 1997, 11-16. |
| APA | Chen C.L.Philip., Cao Y.., & LeClair Steven R. (1997). Functional approximation using regularized orthogonal functional basis neural network. Intelligent Engineering Systems Through Artificial Neural Networks, 7, 11-16. |
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