Gutiérrez P.A. , Hervás C. , CARBONERO RUZ, MARIANO, Fernández J.C.
No
Adv. Soft Comput.
Conference Paper
Científica
0.129
01/01/2007
2-s2.0-61349197621
This paper describes a methodology for constructing the hidden layer of a feed forward network using a possible combination of different transfer projection functions (sigmoidal, product) and kernel functions (radial basis functions), where the architecture, weights and node typology is learnt using an evolutionary programming algorithm. The methodology proposed is tested using five benchmark classification problems from well-known machine intelligence problems. We conclude that combined functions are better than pure basis functions for the classification task in several datasets and that the combination of basis functions produces the best models in some other datasets. © 2007 Springer-Verlag Berlin Heidelberg.
Classification; Evolutionary neural networks; Kernel basis functions; Projection basis functions