Predicting hot-strip finish rolling thickness using stochastic configuration networks

Application of neural networks for predicting hot-rolled strip crown

(2019) Jifei Deng et al.   APPLIED SOFT COMPUTING

Using recurrent neural networks with attention for detecting problematic slab shapes in steel rolling

(2019) Niclas Ståhl et al.   APPLIED MATHEMATICAL MODELLING

A New Predictive Model for Strip Crown in Hot Rolling by Using the Hybrid AMPSO-SVR-Based Approach

(2018) Zhen-Hua Wang et al.   STEEL RESEARCH INTERNATIONAL

Agent and Cyber-Physical System Based Self-Organizing and Self-Adaptive Intelligent Shopfloor

(2017) Yingfeng Zhang et al.   IEEE Transactions on Industrial Informatics

Robust stochastic configuration networks with kernel density estimation for uncertain data regression

(2017) Dianhui Wang et al.   INFORMATION SCIENCES

Prediction of bending force in the hot strip rolling process using artificial neural network and genetic algorithm (ANN-GA)

(2017) Zhen-Hua Wang et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

High Dimensional Data-driven Optimal Design for Hot Strip Rolling of C–Mn Steels

(2017) Siwei Wu et al.   ISIJ INTERNATIONAL

Stochastic Configuration Networks: Fundamentals and Algorithms

(2017) Dianhui Wang et al.   IEEE Transactions on Cybernetics

A T-wave alternans assessment method based on least squares curve fitting technique

(2016) Xiangkui Wan et al.   MEASUREMENT

Application of artificial neural networks for the prediction of roll force and roll torque in hot strip rolling process

(2012) Mahdi Bagheripoor et al.   APPLIED MATHEMATICAL MODELLING

Tension and Thickness Control Strategy Analysis of Two Stands Reversible Cold Rolling Mill

(2012) Guang-ming LIU et al.   JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL