Backpropagation method with type-2 fuzzy weight adjustment for neural network learning
Fernando Gaxiola, Patricia Melín, Fevrier Valdez · 2012
In this paper a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights. In this work an ensemble neural network of three neural networks and average integration for obtain the final result is present. The proposed approach is applied to a case of time series prediction.