一种改进的T-S模糊模型建模及优化方法

Improved Modeling and Optimization of T-S Fuzzy Models

模糊建模是一种有效的非线性系统建模方法,因为非线性系统的复杂性,仍有很多问题难以处理。针对T-S模糊模型,提出了一种改进的建模及优化方法。首先,将快速搜索密度峰聚类和模糊C均值聚类(FCM)算法相结合,使用快速搜索密度峰聚类算法找到聚类个数和初始聚类中心后,再用FCM算法进行聚类;然后,通过最小二乘法辨识结论参数得到初始T-S模糊模型,使用改进的差分进化(DE)算法整体优化模型的结构和参数,获得最终的T-S模型;最后,选择代表性实例,使用MATLAB程序进行仿真分析和比较,验证了本文方法能有效提高T-S模糊模型的辨识精度和速度。

Fuzzy modeling is an effective method for nonlinear systems,but there exist many unsolved issues due to the complexity of nonlinear system.This paper proposes an improved modeling and optimizing method for T-S fuzzy models.Firstly,we combine the fast search method of density peaks with the fuzzy cluster method（FCM）,in which the former is utilized to find the initial clustering center and then the latter achieves the cluster.Secondly,the initial T-S fuzzy model is obtained by using the least square method to identify these parameters.And then,an improved differential evolution algorithm is utilized to optimize the above structure and parameters.Finally,the experimental results over a representative example show that the proposed method can improve the identification precision and convergence speed for T-S fuzzy model.