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ISNN
2004
Springer
15 years 9 months ago
Geometric Interpretation of Nonlinear Approximation Capability for Feedforward Neural Networks
This paper presents a preliminary study on the nonlinear approximation capability of feedforward neural networks (FNNs) via a geometric approach. Three simplest FNNs with at most f...
Bao-Gang Hu, Hong-Jie Xing, Yujiu Yang
PDCAT
2004
Springer
15 years 9 months ago
An Enhanced Fuzzy Neural Network
In this paper, we propose a novel approach for evolving the architecture of a multi-layer neural network. Our method uses combined ART1 algorithm and Max-Min neural network to self...
Kwang-Baek Kim, Young Hoon Joo, Jae-Hyun Cho
IJCNN
2000
IEEE
15 years 8 months ago
Evolving Neural Network Structures Using Axonal Growth Mechanisms
In the eld of arti cial evolution creating methods to evolve neural networks is an important goal. But how to encode the structure and properties of the neural network in the geno...
Peter Eggenberger
ANSS
1998
IEEE
15 years 8 months ago
On Interval Weighted Three-Layer Neural Networks
In solving application problems, the data sets used to train a neural network may not be hundred percent precise but within certain ranges. Representing data sets with intervals, ...
Mohsen Beheshti, Ali Berrached, André de Ko...
ESANN
2008
15 years 5 months ago
Neural networks for computational neuroscience
Computational neuroscience is an appealing interdisciplinary domain, at the interface between biology and computer science. It aims at understanding the experimental data obtained...
David Meunier, Hélène Paugam-Moisy