A New Nonlinear Recurrent Type-2 Fuzzy Model to Identify the Behavior of Nonlinear Dynamic Systems

In this paper, a new recurrent type-2 fuzzy neural network for nonlinear dynamic systems identification is presented. The structure of the new type-2 fuzzy neural network with the non-linear "then" part has 8 layers. In layers 0, 1 and 2, the fuzzification operation is performed and the upper and lower limits of the membership degree are determined. Normalization and weighting operations are performed in layers 3 and 4. In layer 5, there are non-linear trigonometric functions, which actually form the "then" part of the fuzzy system, and return feedback from the output layer enters this layer. Finally, in the 6th and 7th layers, the de-fuzzification operation and the output calculation are performed. In order to check and evaluate the performance of the network in system identification, the input-output information of two physical systems (a DC motor and a flexible robot arm) has been applied to the type-2 recurrent fuzzy neural network. This research is completely experimental and practical, in other words, it is the use of artificial intelligence techniques in operational work. Among the innovations of this article, in addition to presenting a new neural network, is generating a suitable signal to stimulate the system, extracting data from practical systems, data pre-processing (removing outliers, estimating missing data, and normalizing data). In the simulation, the root mean square error criterion shows that the proposed method has a better performance than other methods.