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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.<\/jats:p>","DOI":"10.1155\/2019\/9852134","type":"journal-article","created":{"date-parts":[[2019,5,22]],"date-time":"2019-05-22T00:49:50Z","timestamp":1558486190000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Quality Prediction Model Based on Novel Elman Neural Network Ensemble"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3040-8640","authenticated-orcid":false,"given":"Lan","family":"Xu","sequence":"first","affiliation":[]},{"given":"Yuting","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,5,21]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ces.2013.01.058"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcm.2011.11.021"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2010.2077279"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2013.07.072"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2011.04.006"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcm.2012.12.023"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2017.01.004"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-016-7812-9"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/su10010085"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2016.05.017"},{"key":"e_1_2_9_11_2","article-title":"Multi-model quality prediction approach using fuzzy c-means clustering and support vector regression","volume":"9","author":"Zhang M.","year":"2017","journal-title":"Advances in Mechanical Engineering"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-011-3353-z"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2010.2103401"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2018.10.004"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ces.2018.06.035"},{"key":"e_1_2_9_16_2","first-page":"1","article-title":"Using regression models for predicting the product quality in a tubing extrusion process","author":"Garc\u00eda V.","year":"2018","journal-title":"Journal of Intelligent Manufacturing"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"ParkH. 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