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GECCO
2003
Springer
268views Optimization» more  GECCO 2003»
13 years 10 months ago
A Generalized Feedforward Neural Network Architecture and Its Training Using Two Stochastic Search Methods
Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks in which the synaptic interactions are mediated via a nonlinear mechanism called shuntin...
Abdesselam Bouzerdoum, Rainer Mueller
GECCO
2003
Springer
153views Optimization» more  GECCO 2003»
13 years 10 months ago
SEPA: Structure Evolution and Parameter Adaptation in Feed-Forward Neural Networks
Abstract. In developing algorithms that dynamically changes the structure and weights of ANN (Artificial Neural Networks), there must be a proper balance between network complexit...
Paulito P. Palmes, Taichi Hayasaka, Shiro Usui
IWANN
2005
Springer
13 years 10 months ago
Role of Function Complexity and Network Size in the Generalization Ability of Feedforward Networks
The generalization ability of different sizes architectures with one and two hidden layers trained with backpropagation combined with early stopping have been analyzed. The depend...
Leonardo Franco, José M. Jerez, José...
ICGA
1993
145views Optimization» more  ICGA 1993»
13 years 6 months ago
Genetic Programming of Minimal Neural Nets Using Occam's Razor
A genetic programming method is investigated for optimizing both the architecture and the connection weights of multilayer feedforward neural networks. The genotype of each networ...
Byoung-Tak Zhang, Heinz Mühlenbein
IJCNN
2000
IEEE
13 years 9 months ago
Input Window Size and Neural Network Predictors
Neural Network approaches to time series prediction are briefly discussed, and the need to specify an appropriately sized input window identified. Relevant theoretical results fro...
Ray J. Frank, Neil Davey, S. P. Hunt