Title :
Approximation by random networks with bounded number of layers
Author :
Gelenbe, Erol ; Mao, Zhi-Hong ; Li, Yan-Da
Author_Institution :
Dept. of Comput. Sci., Univ. of Central Florida, Orlando, FL, USA
Abstract :
This paper discusses the function approximation properties of the Gelenbe random neural network (GNN). We use an extension of the basic model: the bipolar GNN (BGNN). We limit the networks to being feedforward and consider the case where the number of hidden layers does not exceed the number of input layers. We show that the feedforward BGNN with s hidden layers (total of s+2 layers) can uniformly approximate continuous functions of s variables
Keywords :
feedforward neural nets; function approximation; learning (artificial intelligence); Gelenbe random neural network; bipolar neural network; continuous functions; hidden layers; input layers; Automation; Biological neural networks; Biological system modeling; Computer science; Function approximation; Mathematical model; Neural networks; Neurons; Polynomials; Recurrent neural networks;
Conference_Titel :
Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop.
Conference_Location :
Madison, WI
Print_ISBN :
0-7803-5673-X
DOI :
10.1109/NNSP.1999.788135
