A Hybrid Robust ANFIS Based on Noise Fuzzy Clustering

Abstract

Adaptive Network-based Fuzzy Inference System (ANFIS) is a promising model of explainable neural networks but rejection of illegal noise effects is an important issue in real application. In this paper, a novel approach for introducing noise clustering concepts into fuzzy $c$-means-based ANFIS is proposed for robust modeling. In the premise part, noise fuzzy clustering is performed in the input data space for estimating fuzzy membership functions removing noise inputs. Then, in the consequence part, rule-wise robust regression models are estimated by removing noise outputs. As a result, the proposed hybrid robust ANFIS model simultaneously considers two types of noise generation schemes of the input-level and the output-level. The characteristics of the proposed method are demonstrated through numerical experiments such that input-level noise are rejected by degrading premise fuzzy memberships of noise objects so that their ANFIS outputs have small absolute values while output-level noise observations are rejected through robust regression.