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.
Highlights
The classical FS including both the T1FS [1] and T2FS [2]are defined using the membership functions
With intuitionistic FS (IFS), a set can be described by three components namely: membership function, non-membership function and hesitation index
In [11], a hybrid model of gradient descent (GD) back-propagation and decoupled EKF (DEKF) is employed for the adjustment of the parameters of IT2IFLS and the model applied to system identification problem.Recently Yuan and Luo [12] proposed an online evolving interval type-2 intuitionistic fuzzy LSTM-Neural Networks
Summary
The classical FS including both the T1FS [1] and T2FS [2]are defined using the membership functions. This implies that to compute the non-membership of a classical FS, one has to take the complement of the set so defined This may not always be the case in real life contexts because people are often times hesitant to pin-point or specify a single numerical value as doing so indicate strong commitments or evidence. This brings the idea of intuitionistic FS (IFS) introduced by Atanassov in 1986 [4]. The IT2IFLS relaxes the single restriction of the IVIFS by allowing two constraints namely that the sum of upper membership and lower non-membership must not exceed 1, the sum of lower membership and upper non-membership must not exceed 1.
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