Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Particle Swarm Optimization of Feed-Forward Neural Networks with Weight Decay
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Group search optimizer: an optimization algorithm inspired by animal searching behavior
IEEE Transactions on Evolutionary Computation
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
Hybrid Training Method for MLP: Optimization of Architecture and Training
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Data mining applied to the cognitive rehabilitation of patients with acquired brain injury
Expert Systems with Applications: An International Journal
Elderly activities recognition and classification for applications in assisted living
Expert Systems with Applications: An International Journal
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Social class differences in the prevalence of Common Mental Disorder (CMD) are likely to vary according to time, culture and stage of economic development. The present study aimed to investigate the use of optimization of architecture and weights of Artificial Neural Network (ANN) for identification of the factors related to CMDs. The identification of the factors was possible by optimizing the architecture and weights of the network. The optimization of architecture and weights of ANNs is based on Particle Swarm Optimization with early stopping criteria. This approach achieved a good generalization control, as well as similar or better results than other techniques, but with a lower computational cost, with the ability to generate small networks and with the advantage of the automated architecture selection, which simplify the training process. This paper presents the results obtained in the experiments with ANNs in which it was observed an average percentage of correct classification of individuals with positive diagnostic for the CMDs of 90.59%.