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SBRN
2000
IEEE
14 years 9 days ago
Evolutionary Optimization of RBF Networks
One of the main obstacles to the widespread use of artijcial neural networks is the difJiculty of adequately define valuesfor their free parameters. This article discusses how Rad...
Estefane G. M. de Lacerda, Teresa Bernarda Ludermi...
IJON
2008
88views more  IJON 2008»
13 years 8 months ago
Neural network construction and training using grammatical evolution
The term neural network evolution usually refers to network topology evolution leaving the network's parameters to be trained using conventional algorithms. In this paper we ...
Ioannis G. Tsoulos, Dimitris Gavrilis, Euripidis G...
IPPS
2006
IEEE
14 years 1 months ago
A combined genetic-neural algorithm for mobility management
This work presents a new approach to solve the location management problem by using the location areas approach. A combination of a genetic algorithm and the Hopfield neural netwo...
Javid Taheri, Albert Y. Zomaya
NN
2000
Springer
145views Neural Networks» more  NN 2000»
13 years 7 months ago
Best approximation by Heaviside perceptron networks
In Lp-spaces with p [1, ) there exists a best approximation mapping to the set of functions computable by Heaviside perceptron networks with n hidden units; however for p (1, ) ...
Paul C. Kainen, Vera Kurková, Andrew Vogt
GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
14 years 1 months ago
Co-evolving recurrent neurons learn deep memory POMDPs
Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
Faustino J. Gomez, Jürgen Schmidhuber