Sept. 17, 2007 to Sept. 19, 2007
Marcio Carvalho , Federal University of Pernambuco
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/HIS.2007.45
The optimization of architecture and weights of feed forward neural networks is a complex task of great importance in problems of supervised learning. In this work we analyze the use of the Particle Swarm Optimization algorithm for the optimization of neural network architectures and weights aiming better generalization performances through the creation of a compromise between low architectural complexity and low training errors. For evaluating these algorithms we apply them to benchmark classification problems of the medical field. The results showed that a PSOPSO based approach represents a valid alternative to optimize weights and architectures of MLP neural networks.
Marcio Carvalho, "Particle Swarm Optimization of Neural Network Architectures andWeights", HIS, 2007, Hybrid Intelligent Systems, International Conference on, Hybrid Intelligent Systems, International Conference on 2007, pp. 336-339, doi:10.1109/HIS.2007.45