In this paper, we propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality\nprediction, thus achieving the quality control of designed products at the product design stage. First, the Elman NNparameters are\noptimized using the grasshopper optimization (GRO) method, and then the weighted averagemethod is improved to combine the\noutputs of the individual NNs, where the weights are determined by the training errors. Simulationswere conducted to compare the\nproposed method with other NNmethods and evaluate its performance.The results demonstrated that the proposed algorithm for\nquality prediction obtained better accuracy than other NN methods. In this paper, we propose a novel Elman NN ensemble model\nfor quality prediction during product design. Elman NN is combined with GRO to yield an optimized Elman network ensemble\nmodel with high generalization ability and prediction accuracy.
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