基于神经网络模式识别的板形模糊控制器

提出一种基于CMAC(Cerebellar Model Articulation Controller)神经网络的板形缺陷模式识别方法,并基于模式识别结果设计了板形模糊控制器.将模式识别与控制器设计合二为一,利用CMAC神经网络识别出相对于6种常见板形缺陷基本模式的隶属度,直接作为板形模糊控制器的前件部,实现了隶属度的求取功能.通过对板形缺陷特征的分析,合理定义了模糊集合,大大地减少了模糊推理的计算量.仿真结果表明,该板形模式识别方法识别精度高,设计的板形模糊控制器可以快速将板形缺陷控制到期望目标,板形控制性能良好.

CMAC-Based Fuzzy Controller for Strip Flatness Pattern Recognition

Based on CMAC (cerebellar model articulation controller) neural network, a pattern recognition method for strip flatness is proposed with a flatness fuzzy controller based on the recognized results designed. Thus, the pattern recognition and controller design are combined into one, i.e., The CMAC is used to recognize the membership levels in regard to six basic patterns of common defects in flatness and then, as the direct forepiece of fuzzy controller, serve for seeking these membership levels. Analyzing the characteristics of defect in flatness, the fuzzy set is defined rationally to reduce greatly the calculation of fuzzy reasoning. The simulation result showed that the pattern recognition method of flatness offers high recognizing precision with which the designed fuzzy controller for flatness can control a defect to an expected extent with satisfactory controllability.