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IJON
2007
118views more  IJON 2007»
13 years 7 months ago
Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm
To predict the 100 missing values from a time series of 5000 data points, given for the IJCNN 2004 time series prediction competition, recurrent neural networks (RNNs) are trained...
Xindi Cai, Nian Zhang, Ganesh K. Venayagamoorthy, ...
IWANN
2007
Springer
14 years 1 months ago
A Comparison Between ANN Generation and Training Methods and Their Development by Means of Graph Evolution: 2 Sample Problems
Abstract. This paper presents a study in which a new technique for automatically developing Artificial Neural Networks (ANNs) by means of Evolutionary Computation (EC) tools is com...
Daniel Rivero, Julian Dorado, Juan R. Rabuñ...
CEC
2007
IEEE
14 years 2 months ago
Improving generalization capability of neural networks based on simulated annealing
— This paper presents a single-objective and a multiobjective stochastic optimization algorithms for global training of neural networks based on simulated annealing. The algorith...
Yeejin Lee, Jong-Seok Lee, Sun-Young Lee, Cheol Ho...
TNN
2008
95views more  TNN 2008»
13 years 7 months ago
A Constrained Optimization Approach to Preserving Prior Knowledge During Incremental Training
In this paper, a supervised neural network training technique based on constrained optimization is developed for preserving prior knowledge of an input
Silvia Ferrari, Mark Jensenius
BMCBI
2006
146views more  BMCBI 2006»
13 years 7 months ago
Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influen...
Michael Meissner, Michael Schmuker, Gisbert Schnei...