基于PSO-Elman模型的火电厂烟气含氧量预测

针对火电厂烟气含氧量测量精度较低、测量成本较大等问题,提出基于PSO-Elman网络模型的烟气含氧量预测方法。选择合理的相关辅助变量,引入Elman神经网络建立辅助变量与烟气含氧量的关系模型;利用PSO对Elman中所有的权值、阈值进行寻优,将其最优权值和阈值作为初值赋给Elman;经过训练,建立基于PSO-Elman模型的预测模型,完成火电厂烟气含氧量的软测量。通过仿真,与Elman网络和LSSVM模型的预测结果作对比,所提模型具有较高的预测精度和良好的泛化能力。

Prediction of the Oxygen Content in Flue Gas of Power Plant Based on PSO-Elman Model

Aiming at such problems of inaccuracy and large costs in current measuring the oxygen content in flue gas of power plant,a prediction model combining Elman neural network and Parti cle Swarm Optimization(PSO-Elman)was proposed. Select reasonable correlative variables and the Elman neural network is introduced to establish the prediction model of relevant variables and oxygen content in flue gas. Use PSO algorithm to optimize the weights and thresholds of the Elman neural network which would be assigned to this neural network. By training,the prediction model of the oxygen content in flue gas of power plant is established based on PSO-Elman network model. Through simulations of specific examples,this prediction model is proved that it has a high accuracy as well as generalization ability in the operation of the power plant being compared with the prediction result of the Elman network and the LSSVM model.