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HAIS
2008
Springer
13 years 9 months ago
An Evolutionary Approach for Tuning Artificial Neural Network Parameters
The widespread use of artificial neural networks and the difficult work regarding the correct specification (tuning) of parameters for a given problem are the main aspects that mot...
Leandro M. Almeida, Teresa Bernarda Ludermir
BMCBI
2006
146views more  BMCBI 2006»
13 years 8 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...
TNN
2008
181views more  TNN 2008»
13 years 7 months ago
Optimized Approximation Algorithm in Neural Networks Without Overfitting
In this paper, an optimized approximation algorithm (OAA) is proposed to address the overfitting problem in function approximation using neural networks (NNs). The optimized approx...
Yinyin Liu, Janusz A. Starzyk, Zhen Zhu
IJCNN
2006
IEEE
14 years 1 months ago
Online Training of a Generalized Neuron with Particle Swarm Optimization
— Neural networks are used in a wide number of fields including signal and image processing, modeling and control and pattern recognition. Some of the most common type of neural ...
Raveesh Kiran, Sandhya R. Jetti, Ganesh K. Venayag...
APIN
1998
139views more  APIN 1998»
13 years 7 months ago
Evolutionary Learning of Modular Neural Networks with Genetic Programming
Evolutionary design of neural networks has shown a great potential as a powerful optimization tool. However, most evolutionary neural networks have not taken advantage of the fact ...
Sung-Bae Cho, Katsunori Shimohara