【摘要】 由于传统模糊建模方法中模型参数都是根据经验选取的,它对于不同系统的动态跟踪能力不同,泛化能力差。针对常规模糊推理的局限性,提出一种类高斯隶属函数,证明了基于类高斯隶属函数的自适应模糊推理系统以精度逼近任意非线性系统。设计了自适应模糊推理系统的结构和参数调整方案,利用梯度下降算法学习模型中的参数,仿真验证了自适应模糊推理模型的逼近性能。更多还原

【Abstract】 For traditional fuzzy inference modeling methods,model parameters are selected based on experience,the model is also determined,which has the different dynamic tracking capability for different systems,so has bad generalization ability.Considering the limitations of conventional fuzzy inference modeling,here a kind of Gaussian-type membership function is proposed,and that adaptive fuzzy inference system based on Gaussian-type membership can approximate nonlinear systems at arbitrary precision is proved.Design of adaptive fuzzy inference system structure and parameters adjustment program,adopt gradient decent algorithm to study model parameters,and then combine with simulation experiments show its universal approximation.Finally,fuzzy inference model applies to nonlinear dynamic system identification and further verifies its feasibility.更多还原