A dual-population multiobjective co-evolutionary matching ensemble learning for product multi-indicator prediction in continuous annealing

Author:

Wang Yao,Wang XianpengORCID

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference55 articles.

1. Multi-objective ensemble learning with multi-scale data for product quality prediction in iron and steel industry;Wang;IEEE Trans. Evol. Comput.,2023

2. A data-driven approach of product quality prediction for complex production systems;Ren;IEEE Trans. Ind. Inform.,2021

3. A wide-deep-sequence model-based quality prediction method in industrial process analysis;Ren;IEEE Trans. Neural Netwo. Learn. Syst.,2020

4. Multi-model ensemble prediction model for carbon efficiency with application to iron ore sintering process;Hu;Control Eng. Pract.,2019

5. An ensemble ELM based on modified AdaBoost.RT algorithm for predicting the temperature of molten steel in ladle furnace;Tian;IEEE Trans. Autom. Sci. Eng.,2010

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