# [1]曹奔,袁忠于,刘洪.基于粒子群算法的烧结炉系统辨识及神经网络控制[J].郑州大学学报(工学版),2017,38(05):39-43.[doi:10.13705/j.issn.1671-6833.2017.02.022] 　Cao Ben,Yuan Zhong,Yu Liu Hong.Sintering Furnace System Identification Based on Particle Swarm Algorithm and Neural Network Control[J].Journal of Zhengzhou University (Engineering Science),2017,38(05):39-43.[doi:10.13705/j.issn.1671-6833.2017.02.022] 点击复制 基于粒子群算法的烧结炉系统辨识及神经网络控制() 分享到： var jiathis_config = { data_track_clickback: true };

38卷

2017年05期

39-43

2017-09-26

## 文章信息/Info

Title:
Sintering Furnace System Identification Based on Particle Swarm Algorithm and Neural Network Control

Author(s):
School of Mechanical and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu, 730070

Keywords:
DOI:
10.13705/j.issn.1671-6833.2017.02.022

A

Abstract:
During heating process of sintering furnace,the model parameters were easy to change,and traditional PID control was difficult to achieve the desired control effect.This paper used particle swarm optimization algorithm to identify the mathematical model of sintering furnace,for sintering furnace with high inertia,time-variation and strong time delay etc,a method of supervision and control based on RBF neural network,which combined PID control with neural network control.When temperature or parameters changed greatly,PID control played a major role.neural network played a regulatory role and compensated the shortage of PID control.The simulation results of MATLAB software showed that this method could improve the control precision of sintering furnace,which had a certain practicality.

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