基于Elman神经网络的联合循环机组燃烧室温度模型建模

doi:10.16180/j.cnki.issn1007-7820.2021.03.011

电子科技 ›› 2021, Vol. 34 ›› Issue (3): 60-64.doi: 10.16180/j.cnki.issn1007-7820.2021.03.011

基于Elman神经网络的联合循环机组燃烧室温度模型建模

窦征立,王亚刚

上海理工大学光电信息与计算机工程学院,上海 200093

收稿日期:2019-12-03 出版日期:2021-03-15 发布日期:2021-03-10
作者简介:窦征立(1992-),男,硕士研究生。研究方向:神经网络、系统辨识。|王亚刚(1967-),男,博士,教授。研究方向:系统辨识、先进过程控制等。
基金资助:
国家自然科学基金(61074087)

Modeling of Combustion Chamber Temperature Model of Combined Cycle Unit Based on Elman Neural Network

DOU Zhengli,WANG Yagang

School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China

Received:2019-12-03 Online:2021-03-15 Published:2021-03-10
Supported by:
National Natural Science Foundation of China(61074087)

摘要/Abstract

摘要：

燃气-蒸汽联合循环机组燃烧室温度模型具有非线性、强耦合的特点,难以建立其精确的过程控制模型。针对这一问题,文中提出了一种基于Elman神经网络的燃烧室温度模型建模。该模型利用不同输入下的输出响应作为训练集数据,利用了Elman神经网络具有以任意精度逼近非线性系统的优点对Elman神经网络进行训练。该模型还利用BPTT算法对误差随时间进行反向传播,并利用SGD算法对网络权值进行优化。试验结果表明,新模型的各项指标均优于原传递函数模型,Elman神经网络模型在单位阶跃输入信号和单位斜坡输入信号下的ITAE指标分别为16.103 4、8.990 1,输出跟踪输入的误差分别为0.039%和0.035%。

关键词: Elman神经网络, 联合循环, 燃烧室, 温度模型, 非线性系统, ITAE

Abstract:

The combustion chamber temperature model of a gas-steam combined cycle unit is nonlinear and strongly coupled, so it is difficult to establish an accurate process control model.To solve the problem, an Elman neural network-based combustion chamber temperature model is proposed in this study. This model uses the output response under different inputs as training set data, and uses the advantage of Elman neural network to approximate non-linear systems with arbitrary accuracy to train Elman neural network. The BPTT algorithm is adopted to back-propagate errors over time, and the SGD algorithm is utilized to optimized network weights. The experimental results show that each index is better than the original transfer function model. The ITAE index of the Elman neural network model under the unit step input signal and the unit ramp input signal are 16.103 4 and 8.990 1, respectively, and the errors of the outputs tracking inputs are 0.039% and 0.035%.

Key words: Elman neural network, combined cycle, combustion chamber, temperature model, non-linear system, ITAE

中图分类号:

TP183

窦征立,王亚刚. 基于Elman神经网络的联合循环机组燃烧室温度模型建模[J]. 电子科技, 2021, 34(3): 60-64.

DOU Zhengli,WANG Yagang. Modeling of Combustion Chamber Temperature Model of Combined Cycle Unit Based on Elman Neural Network[J]. Electronic Science and Technology, 2021, 34(3): 60-64.

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基于Elman神经网络的联合循环机组燃烧室温度模型建模

Modeling of Combustion Chamber Temperature Model of Combined Cycle Unit Based on Elman Neural Network

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输入类型	Elman神经网络模型	原模型
单位阶跃输入	16.103 4	745.500 6
单位斜坡输入	8.990 1	632.993 7

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