The impact of FOU size and number of MFs on the prediction performance of Interval Type-2 Fuzzy Logic Systems

机译：FOU大小和MF数量对区间2型模糊逻辑系统的预测性能的影响

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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.

机译：区间2型模糊逻辑系统（IT2FLS）中包含不确定性足迹（FOU），使其适合于对不确定性进行建模。本文研究了FOU大小和隶属函数（MF）数量对模型的预测性能的影响。这里设计了使用快速学习方法训练的IT2FLS。通过设计具有不同FOU大小的IT2FLS可以捕获数据的不确定性。然后，将极限学习机（ELM）的概念用于IT2FLS结果参数的最佳调整。设计的模型应用于混沌时间序列预测。在仿真过程中，可以观察到，随着MF数量的增加，FOU大小的增加给出了更好的预测结果。

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来源
《2015 International Symposium on Mathematical Sciences and Computing Research》|2015年|104-109|共6页
会议地点 Ipoh(MY)
作者
Saima Hassan; Abbas Khosravi; Jafreezal Jaafar;
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作者单位

Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Perak, Malaysia, 31750;

Centre for Intelligent Systems Research, Deakin University, VIC, 3217, Australia;

Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Perak, Malaysia, 31750;

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原文格式 PDF
正文语种 eng
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关键词
Uncertainty; Fuzzy logic; Time series analysis; Fuzzy sets; Training; Predictive models; Computational modeling;

机译：不确定度;模糊逻辑;时间序列分析;模糊集;训练;预测模型;计算模型;

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4. The impact of FOU size and number of MFs on the prediction performance of Interval Type-2 Fuzzy Logic Systems [C] . Saima Hassan, Abbas Khosravi, Jafreezal Jaafar International Symposium on Mathematical Sciences and Computing Research . 2015

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The impact of FOU size and number of MFs on the prediction performance of Interval Type-2 Fuzzy Logic Systems

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