Interval Type-2 Intuitionistic Fuzzy Logic System for Time Series and Identification Problems - A Comparative Study

Abstract

This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models. KEYWORDSInterval type-2 intuitionistic fuzzy set, Sliding mode control algorithm, Intuitionistic fuzzy set, Intuitionistic fuzzy index.

Software Fault Prediction Based on Interval Type-2 Intuitionistic Fuzzy Logic System

“…However, because the IFS of type-1 is designed around a single point of view and may not capture various forms of uncertainties, Eyoh et al [31] presented a rule-based interval type-2 intuitionistic fuzzy logic system (IT2IFLS) that are defined using fuzzy MFs and NMFs together with hesitation indexes. The IT2IFLS has successfully been applied to solve many applications in different domains with impressive performance such as load forecasting [35], time series prediction [36], [37], [38], [39], [40], [41], identification and prediction problems [31], [42], [43], regression problems [44], [45] transportation problems [46] and many more. To the best knowledge of the authors, this is the first study that adopts IT2IFLS with MF, NMF and hesitation indices for software fault prediction.…”