Intuitionistic Neo-Fuzzy Network for modeling of nonlinear systems dynamics

In this paper, an approach to design an Intuitionistic Neo-Fuzzy Network (INFN) is presented. The proposed architecture combines the advantages of the Intuitionistic Fuzzy Logic (IFL) to deal with uncertainties and the Neo-Fuzzy Neural Network approach to represent nonlinear systems with topologies including small number of parameters. As a learning approach for the consequent fuzzy rules parameters, the gradient optimization procedure is proposed. The investigate the potentials of the generated INF structure, the modeling of a three benchmark chaotic time series - Mackey-Glass, Lorenz and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its further extension to Model Predictive Control is investigated too.