Abstract
Efficient and accurate predication of mechanical properties is the key to controlling the production process. In this paper, a novel Prediction Interval (PI) based method is proposed for forecasting strip steel properties. It specifically consists of a Lower Upper Bound Estimation (LUBE) technique for PI generation based on Particle Swarm Optimization (PSO) and a Coverage Width Symmetry-based Criterion (CWSC) for PI evaluation. To evaluate the proposed method, computational experiments are carried out on two numerical datasets and two real-world datasets from a strip steel production process. A comparison between the results obtained by this work and previous work shows that the proposed method is viable and achieves more advantages. Moreover, the PI constructed on the real-world datasets achieve better quality, demonstrating that the proposed method has good potential in real-world problems.
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Xie, T., Peng, G., Wang, H. (2020). Interval Construction and Optimization for Mechanical Property Forecasting with Improved Neural Networks. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_19
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DOI: https://doi.org/10.1007/978-3-030-29933-0_19
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