Hervas, Cesar , MARTÍNEZ, FATIMA BELÉN, CARBONERO RUZ, MARIANO, Romero, Cristobal , Fernandez, Juan Carlos
No
Bio-Inspired Modeling Of Cognitive Tasks, Pt 1, Proceedings
Proceedings Paper
Científica
01/01/2007
000247802100045
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.