The requirement to manage complicated nonlinear systems with high uncertainty is one of
the main drivers of progress in the identification and control discipline. Since most of the …

This paper describes the architecture and training procedure of a recurrent fuzzy system
(RFS). The RFS is composed of a fuzzy inference system (FIS) and a delayed feedback …

This paper presents a self-adaptive recurrent neuro-fuzzy inference system (R-SANFIS) for
dealing with dynamic problems. The proposed recurrent system possesses two salient …

In this paper, a dynamic recurrent fuzzy neural network (DRFNN) with a structure learning
scheme is proposed. The structure learning scheme consists of two learning phases: the …

In this paper, a new encoding scheme is presented for learning the Takagi–Sugeno (T–S)
fuzzy model from data by genetic algorithms (GAs). In the proposed encoding scheme, the …

This paper proposes a new type fuzzy neural systems, denoted IT2RFNS‐A (interval type‐2
recurrent fuzzy neural system with asymmetric membership function), for nonlinear systems …

A new encoding scheme is presented for a fuzzy-based nonlinear system identification
methodology, using the subtractive clustering and non-dominated sorting genetic algorithm …

In this paper, we propose a Takagi-Sugeno recurrent fuzzy neural network (TSRFNN) for the
identification and control of nonlinear dynamic systems. The TSRFNN combines the …

Many published papers show that a TSK-type fuzzy system provides more powerful
representation than a Mamdani-type fuzzy system. Radial basis function (RBF) network has …

In this paper a new structure of a recurrent neurofuzzy network is proposed. The network is
based on two interconnected Fuzzy Inference Systems (FISs), one recurrent and another …