A New Type of Fuzzy Membership Function Designed for Interval Type-2 Fuzzy Neural Network

作     者：Jiajun Wang 

作者机构：School of Automation Hangzhou Dianzi University Hangzhou 310018 China 

出 版 物：《自动化学报》 (Acta Automatica Sinica)

年 卷 期：2017年第43卷第8期

页      面：1425-1433页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 07[理学] 08[工学] 081104[工学-模式识别与智能系统] 070104[理学-应用数学] 0835[工学-软件工程] 0802[工学-机械工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：国家自然科学基金 

主　　题：模糊隶属函数 模糊神经网络 区间 设计 识别性能 非线性系统 不确定性 调整参数 

摘      要：A new type of fuzzy membership function (FMF) is proposed for interval type-2 fuzzy neural network (IT2FNN) in this paper. Three types of interval type-2 FMF (IT2FMF) can be derived from the proposed type of FMF. And each type of IT2FMF has different shape of footprint of uncertainty (FOU). The derived IT2FMFs are applied to a simplified T2FNN to identify two nonlinear systems. The identification performance of the derived IT2FMFs are compared with Gaussian and ellipsoidal type of IT2FMFs through simulation. Simulation results certify that the derived IT2FMFs can achieve better identification performance than Gaussian and ellipsoidal type of IT2FMFs with elaborately tuning of the parameters for the simplified IT2FNN.