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

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16.1034、8.9901,输出跟踪输入的误差分别为0.039%和0.035%。

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 16.1034 and 8.9901,respectively,and the errors of the outputs tracking inputs are 0.039%and 0.035%.