Intuitionistic Fuzzy Radial Basis Functions Network for modeling of nonlinear dynamics

This paper describes a design methodology for a neural network with improved robust qualities in notion to handling uncertain input data space variations. The proposed network topology combines the simplicity the radial basis functions networks to interpret or classify data pairs and the abilities of the intuitionistic fuzzy logic to deal with the vagueness of the data space. As a learning approach for the designed hybrid neural network, the gradient optimization procedure is proposed. To investigate the potentials of the generated structure throughout varying network parameters, the modeling of a twobenchmark chaotic time series – Mackey-Glass and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its potentials to cope with data variations.