Evolving artificial neural networks using an improved PSO and DPSO


This paper presents an improved particle swarm optimization (PSO) and discrete PSO (DPSO) with an enhancement operation by using a self-adaptive evolution strategies (ES). This improved PSO/DPSO is proposed for joint optimization of three-layer feedforward artificial neural network (ANN) structure and parameters (weights and bias), which is named ESPNet. The experimental results on two real-world problems show that ESPNet can produce compact ANNs with good generalization ability. r 2007 Elsevier B.V. All rights reserved.

DOI: 10.1016/j.neucom.2007.10.013

Extracted Key Phrases

4 Figures and Tables

Citations per Year

200 Citations

Semantic Scholar estimates that this publication has 200 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Yu2008EvolvingAN, title={Evolving artificial neural networks using an improved PSO and DPSO}, author={Jianbo Yu and Shijin Wang and Lifeng Xi}, journal={Neurocomputing}, year={2008}, volume={71}, pages={1054-1060} }