Abstract
In 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.
Highlights
During product design, once the scheme design is complete, designers expect to obtain a reliable estimation of the overall characteristic index of the product system for the purpose of further adjusting the designing parameters and improving overall performance
We propose a novel prediction algorithm based on an improved Elman neural network (NN) ensemble for quality prediction, achieving the quality control of designed products at the product design stage
The Elman NN parameters are optimized using the grasshopper optimization (GRO) method, and the weighted average method is improved to combine the outputs of the individual NNs, where the weights are determined by the training errors
Summary
Once the scheme design is complete, designers expect to obtain a reliable estimation of the overall characteristic index of the product system for the purpose of further adjusting the designing parameters and improving overall performance. To predict the quality of a complex production process, Zhang et al [11] proposed a multimodel modeling approach based on fuzzy C-means clustering and support vector regression to solve the problems of a nonlinear, wide operating condition range and difficult prediction. Avila et al [19] compared the performance of a wide range of statistical models, including a naıve model, multiple linear regression, dynamic regression, regression tree, Markov chain, classification tree, random forest, multinomial logistic regression, and Bayesian network, in the prediction of water quality for the weekly data collected over the summer months from 2006 to 2014 from the Oreti River in Wallacetown in New Zealand.
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