@inproceedings{150abffb78594414824094ff25c4f68b,
title = "Interval Construction and Optimization for Mechanical Property Forecasting with Improved Neural Networks",
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.",
keywords = "Mechanical property forecasting, Neural Network, Prediction Interval",
author = "Tingyu Xie and Gongzhuang Peng and Hongwei Wang",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 19th Annual UK Workshop on Computational Intelligence, UKCI 2019 ; Conference date: 04-09-2019 Through 06-09-2019",
year = "2020",
doi = "10.1007/978-3-030-29933-0\_19",
language = "英语",
isbn = "9783030299323",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "223--234",
editor = "Zhaojie Ju and Dalin Zhou and Alexander Gegov and Longzhi Yang and Chenguang Yang",
booktitle = "Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019",
address = "德国",
}