Abstract
Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method. It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches. The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.
Highlights
The three main types of fuzzy membership function (MF) are Gaussian [1,2], triangular [3,4]and trapezoidal [5,6]
The output of fuzzy inference system (FIS) is evaluated against the other FIS with input MFs from classic IT2FCM and trapezoidal input MFs optimized using genetic algorithm (GA) (IT2 FCMTrapGA)
interval type 2 (IT2) WTLA2 TRAP has promising performance whereby it achieves 75% lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values compared to IT2 fuzzy C means (FCM) Trap-GA and IT2 FCM Gaussian
Summary
The three main types of fuzzy membership function (MF) are Gaussian [1,2], triangular [3,4]and trapezoidal [5,6]. The three main types of fuzzy membership function (MF) are Gaussian [1,2], triangular [3,4]. Each of the MFs has its own advantages and disadvantages, providing better performance than the other MFs in certain scenarios or applications. One way of constructing the MF is through data clustering using fuzzy C means (FCM). FCM is that it generates Gaussian MF, instead of linear MFs such as triangular and trapezoidal. The previous work proposed the generation of trapezoidal MF for the interval type 2 (IT2) fuzzy inference system (FIS) [7]. This paper proposes the extension of the work which is to tune the IT2 trapezoidal MF so that it could produce improved accuracy
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