This paper focuses on the problem of finite-time boundedness and finite-time passivity of discrete-time T-S fuzzy neural networks with time-varying delays. A suitable Lyapunov--Krasovskii functional(LKF) is established to derive sufficient condition for finite-time passivity of discrete-time T-S fuzzy neural networks. The dynamical system is transformed into a T-S fuzzy model with uncertain par...

Fuzzy incidence graphs can be used as models for nondeterministic interconnection networks having extra node-edgerelationships. For example, ramps in a highway system may be modeled as a fuzzy incidence graph so that unexpectedflow between cities and highways can be effectively studied and controlled. Like node and edge connectivity in graphs,node connectivity and arc connectivity in fuzzy inci...

In our previous work we proposed a Multilayer Perceptron Neural Networks (MLP NN) consisting of fuzzy flipflops (F3) based on various operations. We showed that such kind of fuzzy-neural network had good learning properties. In this paper we propose an evolutionary approach for optimizing fuzzy flip-flop networks (FNN). Various popular fuzzy operation and three different fuzzy flip-flop types w...

In this paper we present an algorithm for automatic generation of fuzzy neural networks (FNN). Fuzzy neural networks are concept that integrates some features of the fuzzy logic and the artificial neural networks theory. Based on analysis of several different fuzzy neural networks models, uniform representation method is presented, and two basic types are identified: FNN based on perception fra...

In this paper, we propose the genetic design of fuzzy neural networks with multi-output based on interval type-2 fuzzy set (IT2FSFNNm) for pattern recognition. IT2FSFNNm is the networks of combination between the fuzzy neural networks (FNNs) and interval type-2 fuzzy set with uncertainty. The premise part of the networks is composed of the fuzzy partition of respective input spaces and the cons...

This paper is concerned with the input-output-to-state stability for switched fuzzy neural networks. A new set of matrix norm based conditions is proposed such that switched fuzzy neural networks are input-output-to-state stable. A modified set of conditions for asymptotic stability of switched fuzzy neural networks is also presented in this paper. Keywords— input-output-to-state stability, swi...

an efficient cluster head selection algorithm in wireless sensor networks is proposed in this paper. the implementation of the proposed algorithm can improve energy which allows the structured representation of a network topology. according to the residual energy, number of the neighbors, and the centrality of each node, the algorithm uses fuzzy inference systems to select cluster head. the alg...

in this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. utilizing the generalized characterization theorem. then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. here neural network is considered as a part of large eld called n...

The Fuzzy Constraint Networks model, a generalization of the Disjunctive Temporal Fuzzy Constraint Networks, is a framework that allows representing and reasoning with fuzzy qualitative and quantitative complex constraints. However, its general complexity is exponential, and we need to find tractable subclasses. In this paper we propose two algorithms to deal with a tractable subclass named Ser...