Rolling Force Prediction of Hot Rolling Based on GA-MELM

Jingyi Liu,Ba Tuan Le,Xinxin Liu

doi:10.1155/2019/3476521

Jingyi Liu, Ba Tuan Le + Show 1 more

Open Access

https://doi.org/10.1155/2019/3476521

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Abstract

In the hot continuous rolling process, the main factor affecting the actual thickness of strip is the rolling force. The precision of rolling force calculation is the key to realize accurate on-line control. However, because of the complexity and nonlinearity of the rolling process, as well as many influencing factors, the theoretical analysis of the traditional rolling force prediction model often needs to be simplified and hypothesized. This leads to the incompleteness of the mathematical model and the deviation between the calculated results and the actual working conditions. In this paper, a rolling force prediction method based on genetic algorithm (GA), particle swarm optimization algorithm (PSO), and multiple hidden layer extreme learning machine (MELM) is proposed, namely, PSO-GA-MELM algorithm, which takes MELM as the basic model for rolling force prediction. In the modeling process, GA is used to determine the optimal number of hidden layers and the optimal number of hidden nodes, and PSO is used to search for the optimal input weights and biases. This method avoids the influence of human intervention on the model and saves the modeling time. This paper takes the actual production data of BaoSteel 2050 production line as experimental data, and the experimental results indicate that the algorithm can be effectively used to determine the optimal network structure of MELM. The rolling force prediction model trained by the algorithm has excellent performance in prediction accuracy, computational stability, and the number of hidden nodes and is applicable to the prediction of rolling force in hot continuous rolling process.

Highlights

In the hot continuous rolling production process, the main factor affecting the actual strip thickness is the rolling force, and the accurate calculation of the rolling force is the key to achieve accurate online control [1]
When using the MELM model to predict the rolling force in the hot continuous rolling process, in order to realize the effective design of the MELM network structure, this paper proposes a multiple hidden layer extreme learning machine method based on genetic algorithm and particle swarm optimization algorithm, namely, POS-GA-MELM
MELM is used as the basic model for rolling force prediction, and GA is used to determine the optimal number of hidden layers and the corresponding optimal number of hidden nodes in the MELM network, so as to reasonably select the network structure of the model

Summary

Introduction

In the hot continuous rolling production process, the main factor affecting the actual strip thickness is the rolling force, and the accurate calculation of the rolling force is the key to achieve accurate online control [1]. When using the MELM model to predict the rolling force in the hot continuous rolling process, in order to realize the effective design of the MELM network structure, this paper proposes a multiple hidden layer extreme learning machine method based on genetic algorithm and particle swarm optimization algorithm, namely, POS-GA-MELM. In this method, MELM is used as the basic model for rolling force prediction, and GA is used to determine the optimal number of hidden layers and the corresponding optimal number of hidden nodes in the MELM network, so as to reasonably select the network structure of the model. The rest of this paper is organized as follows: Section 2 presents a brief review of the basic concepts and related work of the original ELM and the multiple hidden layers ELM, Section 3 describes the proposed PSO-GA-MELM technique, Section 4 reports and analyzes the experimental results, and, Section 5 summarizes key conclusions of the present study

Brief Review of ELM and MELM

Proposed PSO-GA-MELM

Experiments and Results

Conclusion