Quality Prediction Model Based on Novel Elman Neural Network Ensemble

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dc.contributor.authorXu, Lan
dc.contributor.authorZhang, Yuting
dc.date.accessioned2019-05-26T07:01:21Z
dc.date.available2019-05-26T07:01:21Z
dc.date.issued2019-05-21
dc.date.updated2019-05-26T07:01:13Z
dc.description.abstractIn this paper, we propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality prediction, thus achieving the quality control of designed products at the product design stage. First, the Elman NN parameters are optimized using the grasshopper optimization (GRO) method, and then the weighted average method is improved to combine the outputs of the individual NNs, where the weights are determined by the training errors. Simulations were conducted to compare the proposed method with other NN methods and evaluate its performance. The results demonstrated that the proposed algorithm for quality prediction obtained better accuracy than other NN methods. In this paper, we propose a novel Elman NN ensemble model for quality prediction during product design. Elman NN is combined with GRO to yield an optimized Elman network ensemble model with high generalization ability and prediction accuracy.
dc.description.versionPeer Reviewed
dc.identifier.citationLan Xu and Yuting Zhang, “Quality Prediction Model Based on Novel Elman Neural Network Ensemble,” Complexity, vol. 2019, Article ID 9852134, 11 pages, 2019. doi:10.1155/2019/9852134
dc.identifier.urihttp://dx.doi.org/10.1155/2019/9852134
dc.identifier.urihttp://hdl.handle.net/1880/110435
dc.identifier.urihttps://dx.doi.org/10.11575/PRISM/36595
dc.language.rfc3066en
dc.rights.holderCopyright © 2019 Lan Xu and Yuting Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.titleQuality Prediction Model Based on Novel Elman Neural Network Ensemble
dc.typeJournal Article
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