The Impact of FOU Size and Number of MFs on the Prediction Performance of Interval Type-2 Fuzzy Logic Systems

被引：0

作者：

Hassan, Saima ^{[1
]}

Khosravi, Abbas ^{[2
]}

Jaafar, Jafreezal ^{[1
]}

机构：

[1] Univ Teknol PETRONAS, Dept Comp & Informat Sci, Perak 31750, Malaysia

[2] Deakin Univ, Ctr Intelligent Syst Res, Geelong, Vic 3217, Australia

来源：

2015 INTERNATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES AND COMPUTING RESEARCH (ISMSC) | 2015年

关键词：

Type-2 Fuzzy Logic Systems; time series forecasting; footprint of uncertainty; extreme learning machine; optimization; OPTIMIZATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The inclusion of footprint of uncertainty (FOU) in Interval Type-2 Fuzzy Logic Systems (IT2FLSs) made them suitable for modelling uncertainty. This paper investigates the impact of FOU size and number of membership functions (MFs) on the model's prediction performance. An IT2FLS trained using a fast learning method is designed here. The uncertainty in data is captured by designing the IT2FLS with different sizes of FOU. The concept of extreme learning machine (ELM) is then used for optimal tuning of IT2FLS consequent parameters. The designed model is applied to the chaotic time series prediction. During simulation it is observed that the increase in FOU size with the increase in number of MFs give better prediction results.

引用

页码：104 / 109

页数：6

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