A new Gaussian fuzzy logic inference system of Takagi-Sugeno-Kanga type with principal component weighting

Dung Thi Thu Nguyen

Abstract


At the present time, fuzzy inference systems have given remarkably effective support in solving many problems in practical applications. Among such systems, the powerful characteristics and applications of the Takagi-Sugeno-Kanga (TSK) fuzzy system are significant. In this study, a new Gaussian fuzzy TSK inference system with the use of principal component analysis method is proposed to minimize the volume capacity of fuzzy logic rule system when the number of input indicators is relatively large, and the same time, the model has enhancements when weight values of input indicators are considered by the proportion of input information contribution. The model applies the entropy minimization approach (MEPA) to support the fuzzification process of a number of input data in an efficient way. The proposed model is applied to forecast the socio-economic development index of 63 provinces in Vietnam, which uses the socio-economic development indicators of 63 provinces in 2019 as input data. The forecasting results of the proposed system are measured and analyzed by comparison of forecasted and actual values, and evaluation of MSE, RMSE, MAPE and CORR values between the model using weighting coefficients and the model not using weighting coefficients. The proposed model using weighting factors has better performance


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