Title
An evolving T-S fuzzy model identification approach based on a special membership function and its application on pump-turbine governing system.
Abstract
Hyper-plane-shaped clustering (HPSC) has been proved to be more effective in Takagi–Sugeno (T–S) fuzzy model identification compared with hyper-sphere-shaped clustering (HSSC). However, there is no special membership function matching HPSC in fuzzy modeling, and the commonly used bell-shaped Gaussian function is more suitable for HSSC. In this paper, a novel T–S fuzzy model identification method is adopted, in which a new fuzzy membership function designed for HSPC is designed. In this approach, a fuzzy c-regression model based clustering method is used to partition the fuzzy space firstly; and then a new HPSC fuzzy membership function is designed to identify the antecedent membership function (MF) parameters; finally the gravitational search algorithm is applied to optimize the MF parameters further. Experimental results on several benchmark problems show that modeling accuracies have been promoted significantly. The proposed approach has been applied in fuzzy modeling of pump-turbine governing system (PTGS). The comparative experimental results reveal that the proposed approach could achieve high accuracy and would be an effective modeling tool for complicated nonlinear system in engineering applications.
Year
DOI
Venue
2018
10.1016/j.engappai.2017.12.005
Engineering Applications of Artificial Intelligence
Keywords
Field
DocType
Hyper-plane-shaped Gaussian function,Fuzzy membership function,T–S fuzzy model,Fuzzy c-regressive model,Pump-turbine governing system,Gravitation search algorithm
Mathematical optimization,Nonlinear system,Computer science,Fuzzy model identification,Fuzzy logic,Turbine,Cluster analysis,Partition (number theory),Gaussian function,Membership function
Journal
Volume
ISSN
Citations 
69
0952-1976
1
PageRank 
References 
Authors
0.36
36
4
Name
Order
Citations
PageRank
Chaoshun Li126215.91
Wen Zou2141.28
Nan Zhang320624.70
Xinjie Lai4212.79