05367634

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05367634 Niu, Ben Li, Li A hybrid particle swarm optimization for feed-forward neural network training. Huang, De-Shuang (ed.) et al., Advanced intelligent computing theories and applications. With aspects of artificial intelligence. 4th international conference on intelligent computing, ICIC 2008, Shanghai, China, September 15--18, 2008. Proceedings. Berlin: Springer (ISBN 978-3-540-85983-3/pbk). Lecture Notes in Computer Science 5227. Lecture Notes in Artificial Intelligence, 494-501 (2008). 2008 Berlin: Springer EN hybrid particle swarm optimization neural network training

doi:10.1007/978-3-540-85984-0_59

Summary: This paper employs a hybrid particle swarm optimization using optimal foraging theory (PSOOFT) for multilayer feed-forward neural network (MFNN) training. Three benchmark classification problems: Iris, Newthyroid and Glass are conducted to measure the performance of PSOOFT based MFNN. The simulation results are also compared with obtained using back Propagation (BP), genetic algorithm (GA) and standard PSO (SPSO) approaches to demonstrate the effectiveness and efficiency of PSOOFT.