| Title |
Evolutionary combining of basis function neural networks for classification |
| Authors |
Hervás C. , MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, CARBONERO RUZ, MARIANO, Romero C. , Fernández J.C. |
| External publication |
No |
| Means |
Lecture Notes in Computer Science |
| Scope |
Conference Paper |
| Nature |
Científica |
| JCR Quartile |
4 |
| SJR Quartile |
2 |
| SJR Impact |
0.293 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-38149050564&partnerID=40&md5=4a955fd79e10c91bc2b9ca7ef723ebcb |
| Publication date |
01/01/2007 |
| Scopus Id |
2-s2.0-38149050564 |
| DOI |
10.1007/978-3-540-73053-8_45 |
| Abstract |
The paper describes a methodology for constructing a possible combination of different basis functions (sigmoidal and product) for the hidden layer of a feed forward neural network, where the architecture, weights and node typology are learned based on evolutionary programming. This methodology is tested using simulated Gaussian data set classification problems with different linear correlations between input variables and different variances. It was found that combined basis functions are the more accurate for classification than pure sigmoidal or product-unit models. Combined basis functions present competitive results which are obtained using linear discriminant analysis, the best classification methodology for Gaussian data sets. © Springer-Verlag Berlin Heidelberg 2007. |
| Keywords |
Computer simulation; Correlation methods; Discriminant analysis; Evolutionary algorithms; Gaussian distribution; Combined basis functions; Gaussian data set classification; Gaussian data sets; Radial basis function networks |
| Universidad Loyola members |
|
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