An Evolutionary Objective Cluster Analysis-based Interpretable Fuzzy identification method

作者：Wang Na; Hu Chaofang; Shi Wuxi

来源：Communications in Computer and Information Science, 2013, 355: 169-177.

DOI：10.1007/978-3-642-37105-9_19

• Electrical applications
• Evolutionary strategies
• Fuzzy identification
• Fuzzy identification method
• Fuzzy modeling
• Interpretable
• Mamdani fuzzy models
• Mamdani model

In this paper, an Evolutionary Objective Cluster Analysisbased Interpretable Fuzzy Identification Method (EOCA-IFIM) is proposed for constructing Mamdani fuzzy model. Firstly, the Enhanced Objective Cluster Analysis (EOCA) algorithm is presented to obtain the robust and the moderate compact initial fuzzy partition. Following, the (1+1) Evolutionary Strategy (ES) is introduced to improve the semantics of the initial parameters. Based on that, a complexity-accuracy trade-off is well realized. The simulation results of the Box-Jenkins and the electrical application example show the superiority of the presented method.

• 出版日期2013
• 单位天津工业大学