Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets

作者：

Highlights：

• We integrate a PSO with a NN and ERNN to form PSO-NN and PSO-ERNN metaheuristic models.

• The PSO trains the NN and ERNN to achieve faster convergence and avoid local minima.

• The PSO-NN and PSO-ERNN models are evaluated using ten benchmark classification datasets.

• The accuracy and computational efficiency of the models are improved.

• The PSO-ERNN model outperforms the PSO-NN model for most of the tested datasets.

摘要

•We integrate a PSO with a NN and ERNN to form PSO-NN and PSO-ERNN metaheuristic models.•The PSO trains the NN and ERNN to achieve faster convergence and avoid local minima.•The PSO-NN and PSO-ERNN models are evaluated using ten benchmark classification datasets.•The accuracy and computational efficiency of the models are improved.•The PSO-ERNN model outperforms the PSO-NN model for most of the tested datasets.

论文关键词：Feedforward neural network,Elman recurrent neural network,Particle swarm optimization,Backpropagation learning,Classification

论文评审过程：Received 16 February 2021, Revised 16 May 2021, Accepted 12 June 2021, Available online 18 June 2021, Version of Record 22 June 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115441