A Neuro-Fuzzy Based Framework for Online Nonlinear System Identification
Paulo Sousa Gil, Tiago Oliveira, Luís Brito Palma · 2017
In this paper a new general recurrent state-space Neuro-Fuzzy model structure based on the combination of a modified Jordan network and an Adaptive Neuro-Fuzzy Inference System is proposed. The Neural-Fuzzy System's online training is carried out based on a Constrained Unscented Kalman Filter, where weights, membership functions and consequents are recursively updated. Results from a benchmark MIMO system demonstrate the applicability and effectiveness of the proposed framework.