基于T—S模型的聚丙烯优化控制

针对模糊控制表达能力强,但学习能力、适应性差的特点,提出在聚合反应过程的温度控制系统中使用模糊控制与神经网络相结合的控制方案,控制方案给出了基于Takagi-Sugeno模型的神经模糊网络结构以及优化模糊规则和隶属函数的学习算法;仿真研究表明,神经模糊网络可以根据输入数据的分布情况,自动修改模糊规则的加权系数和隶属函数,克服了单纯的模糊控制其控制规则和隶属函数一经得到就固定不变的缺陷,具有较强的适应能力和学习能力,较单纯的模糊控制有更高的控制精度.

Optimizing Control of Polymerizing-kettle Based on T-S Model

In view of the characteristics of fuzzy control,such as high expression capability,low learning capability and adaptability,a control method of combining fuzzy control with neural network for polymerizing-kettle temperature control is proposed.The constitution of neural fuzzy network based on T-S model,and an algorithm for optimizing fuzzy rules and membership function are given by the method.The MATLAB's simulation experiments proved that neural fuzzy network could automatically amend weighing parameters o...