Fuzzy systems, neural networks and its combination in neuro-fuzzy systems are already well established in data analysis and system control. Especially, neurofuzzy systems are well suited for the development of interactive data analysis tools, which enable the creation of rule-based knowledge from data and the introduction of a-priori knowledge into the process of data analysis. However, its rec...

This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are forme...

in this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.the proposed method combines the fuzzy c-means clustering method, a recurrent functional neural fuzzy network (rfnfn), and a modified differential evolution.the proposed rfnfn is based on the two backlight factors that can accurately detect the compensat...

This paper presents an approach for stabilization of equilibria in recurrent fuzzy systems. This type of dynamic fuzzy systems being defined via linguistic rules can be interpreted as interpolation between constant gradients, and therefore as hybrid dynamical system. It is shown that the latter viewpoint allows for a precise description of the system dynamics, but on the other hand lacks transp...

Abstrad A Fuzzijed TSK-type Recurrent Neiiral Fuzzy Network (FTRNFN) for handling furry temporal information is proposed in this paper. The inputs and oulputs of FTRNFN are fuzzy pattems represented by Gaussian or isosceles triangular membership functions. In structure, FTRNFN is a recurrent fizzy nefwork constructed from a series of recurrent fuzzy +then rules with TSK-t)pe Consequent parts. T...

in this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the takagi-sugeno (t-s) fuzzy models is considered. a novel linear matrix inequality (lmi)-based stability criterion is obtained by using lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

In this paper we present a simple belief updating system using recurrent fuzzy rules which improves class prediction in ordered datasets. The recurrent fuzzy rule builds up belief in a class for each point in a sample-ordered or timeordered dataset. Belief in each class is represented by a fuzzy set predicted class defined on the class universe. Belief in a class increases as positive cases are...

Recurrent fuzzy neural networks (FNNs) have been widely applied to dynamic system processing problems. However, most recurrent FNNs focus on the use of type-1 fuzzy sets. This paper proposes a Mamdani-type recurrent interval type-2 FNN (M-RIT2FNN) that uses interval type-2 fuzzy sets in both rule antecedent and consequent parts. The reason for using interval type-2 fuzzy sets is to increase net...

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

Two-string fuzzy inference consists of two separate inference mechanisms: One conventional fuzzy inference system that processes recommending rules, as well as a mechanism for processing negative rules, which prevent the system from outputting their associated values when their premise is fulfilled. Two-string inference has valuable applications in pattern recognition and control tasks. We pres...