基于T-S模糊神经网络汽提塔温度控制研究

Research on Control on Temperature of Stripping Tower Based on T-S Fuzzy Neural Network

由于汽提过程是一个具有高度非线性和时变等特点的复杂工艺过程,其准确的数学模型难以建立,过程参数也较难控制,运用传统的控制方法难以达到高精度的控制效果。基于T-S模糊神经网络具有较强的逼近和学习能力,又需要较少的先验知识,能够在线学习等特点,本文采用最邻近聚类法对传统的T-S模糊神经网络方法进行改进,同时,采用共轭梯度法和递归最小二乘法来确定模型得参数,结合现有的该公司的实际运行数据,建立了汽提塔系统的模型。仿真结果验证了采用T-S模糊神经网络方法建模的有效性。

The stripping process is a highly nonlinear and time-varying complex process,it is difficult to establish accurate mathematical model,and process parameters are difficult to control,to achieve the use of traditional control method control effect with high accuracy.Based on T-S fuzzy neural network has a good approximation and learning ability,and less the prior knowledge repured,to online learning characteristics,The nearest neighbor clustering method is applied to improve the traditional T-S fuzzy neural network method in this paper. At the same time,using the conjugate gradient method and the recursive least square method is used to determine the model parameters,combined with the actual operation data of the company＇s existing,stripper system model is established.The simulation results verify the validity of the model.