T2-HyFIS-yager: Type 2 hybrid neural fuzzy inference system realizing yager inference

The hybrid neural fuzzy inference system (Hy-FIS) is a five layers adaptive neural fuzzy inference system, based on the compositional rule of inference (CRI) scheme, for building and optimizing fuzzy models. To provide the HyFIS architecture with a firmer and more intuitive logical framework that emulates the human reasoning and decision-making mechanism, the fuzzy Yager inference scheme, together with the self-organizing Gaussian discrete incremental clustering (gDIC) technique, were integrated into the HyFIS network to produce the HyFIS-Yager-gDIC . This paper presents T2-HyFIS-Yager, a type-2 hybrid neural fuzzy inference system realizing Yager inference, for learning and reasoning with noise corrupted data. The proposed T2-HyFIS-Yager is used to perform time-series forecasting where a non-stationary time-series is corrupted by additive white noise of known and unknown SNR to demonstrate its superiority as an effective neuro-fuzzy modeling technique.