Method for Higher Order polynomial Sugeno Fuzzy Inference Systems

A method for Higher Order polynomial Sugeno Fuzzy Inference Systems formation is presented. Compared to other existing Higher Order Sugeno implementations, it uses fewer parameters; and compared to Zero and 1st Order Sugeno Fuzzy Systems it has overall improved model performance. While best models are not always obtained via a Higher Order representation, in our proposed method it is possible to choose the polynomial Order which best fits the desired data. Its input is a previously established model found by a clustering algorithm (subtractive algorithm in this case). Afterward, parameters of all Higher Order polynomials are adjusted using Recursive Least Square algorithm. For experimental validation, multiple benchmark datasets are tested using Hold-Out and K-fold validation as well as data forecasting. Various performance measures are used, although Akaike Information Criterion is used as a primary measure to demonstrate that our proposed Higher Order polynomials have overall better model performance over Zero and 1st Order polynomials.