作　　者： ; ;

机构地区： 茂名学院机电工程学院自动化系

出　　处： 《自动化仪表》 2008年第6期59-61,64,共4页

摘　　要： 针对模糊控制表达能力强,但学习能力、适应性差的特点,提出在聚合釜温度控制过程中使用模糊控制与神经网络相结合的控制方案。给出了神经模糊网络结构和学习算法,仿真研究表明,神经模糊网络可以直接从经验中获取知识,自动建立模糊规则和隶属函数,无需查表,具有较强的适应和联想能力,比单纯的模糊控制具有更大的优势。 In accordance with the features of fuzzy control, i.e. powerful expression capability but poor learning capability and adaptability, the control strategy combining fuzzy control and neural network control is proposed for temperature control of polymerization still. The structure and learning algorithm of neural fuzzy network is given. The simulated research shows that the neural fuzzy network is able to get knowledge from experience directly then establish fuzzy rules and membership function automatically without table look up procedure. It offers higher adaptability and capability of associating with, thus provides better superiority than that of simple fuzzy control.

关 键 词： 神经模糊网络 聚合釜 仿真 隶属函数 模糊规则

领　　域： [自动化与计算机技术] [自动化与计算机技术]