一种基于CMAC神经网络的模糊控制器设计

对于存在外在的环境干扰和系统参数时变的非线性系统来说,传统的小脑模型需要重新学习合适的权重参数,这种学习式的设计方法是相当耗时的,为了改善这种情况,本文提出了模糊控制与小脑模型结合的方式,能够有效地对未知的非线性模型系统进行实时控制。通过仿真的对比试验,这种把小脑神经网络与模糊控制结合起来的控制方法,具有两种控制方法的优点。仿真结果表明,FCMAC控制器具有较高的控制精度、良好的自适应特性。

Fuzzy controller design based on a CMAC neural network

With regard to nonliear system which contain external environmental interference and time-varying system parameters,the traditional CMAC need to re-study the weight of the appropriate parameters,this study design is very time-consuming.In this thesis,a novel approach of compound fuzzy control and cerebellar model articulation controller(CMAC) can solve the tracking problem of a class of nonlinear systems.Through simulation comparative trial,this compound controller possesses the advantages of the two contro...