摘要
实际工业生产过程中,系统的数据带有测量噪声。Ⅱ型模糊集的二阶隶属度用来表征一阶隶属度的模糊度,这种模糊度的增加意味着处理不确定信息能力增加。因此,提出了一种基于Ⅱ型模糊集的T-S模糊建模方法来减少由噪声带来不确定信息的影响。首先采用改进的最小邻域算法对带有测量噪声的数据进行聚类,继而确定Ⅱ型模糊集的一阶隶属度,接着根据数据的聚类信息采用高斯混和模型得到二阶隶属度值,然后用正交最小二乘算法确定模糊模型的后件参数,最后通过仿真实验来验证该方法的有效性。
In actual industrial processes, the measurement data always contain noise. Therefore, this situation will affect the accuracy of modeling. Compared with the type-1 fuzzy sets, the membership functions in type-2 fuzzy sets include the primary membership function and the secondary membership function. They pro vide the additional degrees of freedom that make it possible to model uncertainties brought by the noises. In this paper, a type-2 T-S fuzzy model is presented to minimize the effect of measurement noises. Furthermore, the influence of the initial conditions is considered in the algorithm. The primary membership function is gained through an improved nearest neighborhood clustering algorithm, and the secondary membership function is determined through GMM based on the sufficient statistics. The orthogonal least-squared algorithm is used to identify the consequent of the fuzzy rules. Finally, the simulation results are compared with those obtained from a type-1 T-S fuzzy modeling results and the superiority of the proposed approach is highlighted.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2008年第10期1957-1961,共5页
Systems Engineering and Electronics
基金
国家自然科学基金资助课题(60604018
60474051)
关键词
Ⅱ型模糊逻辑系统
最小邻域算法
模糊C均值算法
高斯混合模型
EM算法
type 2 fuzzy logic system
nearest-neighborhood clustering algorithm
fuzzy c -means clustering algorithm
GMM
EM algorithm
