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Título : Training Neural Networks by Continuation Particle Swarm Optimization
Autor : Rojas Delgado, Jairo
Trujillo Rasúa, Rafael
Palabras clave : CONTINUATION;OPTIMIZATION;NEURAL- NETWORK;TRAINING
Fecha de publicación : 2018
Editorial : Springer
Citación : Rojas-Delgado J., Trujillo-Rasúa R. (2018) Training Neural Networks by Continuation Particle Swarm Optimization. In: Hernández Heredia Y., Milián Núñez V., Ruiz Shulcloper J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science, vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_7
Resumen : Artificial Neural Networks research field is among the areas of major activity in Artificial Intelligence. Conventional training approaches applied to neural networks present several theoretical and computational limitations. In this paper we propose an approach for Artificial Neural Network training based on optimization by continuation and Particle Swarm Optimization algorithm. The objective is to reduce overall execution time of training without causing negative effects in accuracy. Our proposal is compared with Standard Particle Swarm Optimization algorithm using public benchmark datasets. Experimental results show that the optimization by continuation approach reduces execution time required to perform training in about 20%−50% without statistically significant loss of accuracy.
URI : https://repositorio.uci.cu/jspui/handle/123456789/9480
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