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

Hassan, S. and Khosravi, A. and Jaafar, J. (2016) The impact of FOU size and number of MFs on the prediction performance of Interval Type-2 Fuzzy Logic Systems. In: UNSPECIFIED.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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. © 2015 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 2
Uncontrolled Keywords: Computation theory; Computer circuits; Forecasting; Knowledge acquisition; Learning systems; Membership functions; Optimization; Reconfigurable hardware; Time series; Uncertainty analysis, Chaotic time series prediction; Different sizes; Extreme learning machine; Footprint of uncertainties; Interval type-2 fuzzy logic systems; Prediction performance; Time series forecasting; Type-2 fuzzy logic system, Fuzzy logic
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 07:33
Last Modified: 25 Mar 2022 07:33
URI: http://scholars.utp.edu.my/id/eprint/30801

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