Stability analysis of Takagi–Sugeno fuzzy Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays

In this paper, the global asymptotic stability problem of Takagi–Sugeno (TS) fuzzy Cohen–Grossberg Bidirectional Associative Memory neural networks (FCGBAMNNs) with discrete and distributed time-varying delays is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of FCGBAMNNs which are represented by TS fuzzy models. Our results can be easily verified and are also less restrictive than previously known criteria and can be applied to Cohen–Grossberg neural networks, recurrent neural networks and cellular neural networks. Finally, the proposed stability conditions are demonstrated with a numerical example.     

H∞ control for uncertain Takagi–Sugeno fuzzy systems with time-varying delays and nonlinear perturbations

This paper investigates the robust H_∞ control for Takagi–Sugeno (T–S) fuzzy systems with time-varying delays and nonlinear perturbations. Delay-dependent criteria for uncertain fuzzy systems have been proposed. The LMI optimization approach is applied to solve the H_∞ control problem and find the minimization of H_∞ norm bound. Computer simulations show that significant improvement over the previous results can be obtained.     

Stability and stabilization by output feedback control of positive Takagi–Sugeno fuzzy discrete-time systems with multiple delays

This paper deals with the problem of stabilization by output feedback control of Takagi–Sugeno (T–S) fuzzy discrete-time systems with a fixed delay by linear programming (LP) and cone complementarity while imposing positivity in closed-loop. The stabilization conditions are derived using the single Lyapunov–Krasovskii Functional (LKF). An example of a real plant is studied to show the advantages of the design procedure.     

Stability analysis of fuzzy Markovian jumping Cohen–Grossberg BAM neural networks with mixed time-varying delays

In this paper, we investigate the robust stability of uncertain fuzzy Markovian jumping Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays. A new delay-dependent stability condition is derived under uncertain switching probabilities by Takagi–Sugeno fuzzy model. Based on the linear matrix inequality (LMI) technique, upper bounds for the discrete and distributed delays are calculated using the LMI toolbox in MATLAB. Numerical examples are provided to illustrate the effectiveness of the proposed method.     

LMI conditions for stability of impulsive stochastic Cohen–Grossberg neural networks with mixed delays

In this paper, the global asymptotic stability of impulsive stochastic Cohen–Grossberg neural networks with mixed delays is investigated by using Lyapunov–Krasovskii functional method and the linear matrix inequality (LMI) technique. The mixed time delays comprise both the multiple time-varying and continuously distributed delays. Some new sufficient conditions are obtained to guarantee the global asymptotic stability of the addressed model in the stochastic sense using the powerful MATLAB LMI toolbox. The results extend and improve the earlier publications. Two numerical examples are given to illustrate the effectiveness of our results.     

Dynamical behaviors of reaction–diffusion fuzzy neural networks with mixed delays and general boundary conditions

In this paper, we study delayed reaction–diffusion fuzzy neural networks with general boundary conditions. By using topology degree theory and constructing suitable Lyapunov functional, some sufficient conditions are given to ensure the existence, uniqueness and globally exponential stability of the equilibrium point. Finally, an example is given to verify the theoretical analysis.     

Global robust stability criteria of stochastic Cohen–Grossberg neural networks with discrete and distributed time-varying delays

The paper is concerned with the problem of robust asymptotic stability analysis of stochastic Cohen–Grossberg neural networks with discrete and distributed time-varying delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technology, some sufficient conditions are derived to ensure the global robust convergence of the equilibrium point. The proposed conditions can be checked easily by LMI Control Toolbox in Matlab. Furthermore, all the results are obtained under mild conditions, assuming neither differentiability nor strict monotonicity for activation function. A numerical example is given to demonstrate the effectiveness of our results.     

Upper semicontinuity of random attractors of stochastic discrete complex Ginzburg–Landau equations with time-varying delays in the delay

A system of stochastic discrete complex Ginzburg–Landau equations with time-varying delays is considered. We first prove the existence and uniqueness of random attractor for these equations. Then, we analyze the convergence properties of the solutions as well as the attractors as the length of time delay approaches zero.     

Synchronization analysis for complex networks with coupling delay based on T–S fuzzy theory

In this paper, synchronization problems for a general complex networks are investigated by Takagi–Sugeno (T–S) fuzzy theory. A novel concept named linear approximation method is firstly proposed to solve the synchronization problems for T–S fuzzy complex networks. This novel method can linearize the system into some time-delay subsystems, which can effectively simplify the complicated system and then be easy to acquire the synchronization results. Since the expression based on linear matrix inequality (LMI) is used, the synchronization criteria can be easily checked in practice. Numerical simulation examples are provided to verify the effectiveness and the applicability of the proposed method.     

Robust μ-stability analysis of Markovian switching uncertain stochastic genetic regulatory networks with unbounded time-varying delays

This paper investigates the global robust stability problem of Markovian switching uncertain stochastic genetic regulatory networks with unbounded time-varying delays and norm bounded parameter uncertainties. The structure variations at discrete time instances during the process of gene regulations known as hybrid genetic regulatory networks based on Markov process is proposed. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. The concept of global robust μ-stability in the mean square for genetic regulatory networks is given. Based on Lyapunov function, stochastic theory and Itô’s differential formula, the stability criteria are presented in the form of linear matrix inequalities (LMIs). Numerical examples are presented to demonstrate the effectiveness of the main result.     

Robust stability criteria for uncertain neutral type stochastic system with Takagi–Sugeno fuzzy model and Markovian jumping parameters

In this paper, the robust stability for uncertain neutral stochastic system with Takagi–Sugeno (T–S) fuzzy model and Markovian jumping parameters (MJPs) are investigated. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite-state space. Some novel sufficient conditions are derived to guarantee the asymptotic stability of the equilibrium point in the mean square. By utilizing the Lyapunov–Krasovskii functional, stochastic analysis theory, some free weighting matrices and linear matrix inequality (LMI) technique, the upper bound of time-varying delay is obtained by using Matlab® control toolbox. Finally, some numerical examples are given to show the effectiveness of the obtained results.     

Robust stabilization of multilinear interval plants by Takagi–Sugeno fuzzy controllers

This study investigates the stabilization problem of interval and multilinear interval plants. For this purpose, a Takagi–Sugeno fuzzy state model, expressed in the form of an interval system, is considered as a robust stabilizing controller for the plants with interval and multilinear interval models. Based on the Zero Exclusion Condition, sufficient conditions that ensure robust stability of the presented fuzzy controller are derived as well. A two-mass-spring-damper system with multilinear uncertain parameters is studied to demonstrate the application and the effectiveness of the design process.     

Fault tolerant saturated control for T–S fuzzy discrete-time systems with delays

In this paper, fault tolerant saturated control (FTSC) problem for discrete-time T–S fuzzy systems with delay is studied. Sufficient conditions for observer based control design with saturation constraints are developed. Based on a multiple Lyapunov functions and the slack variables, design conditions are formulated in LMI terms using the cone complementarity technique. The obtained results are illustrated on a numerical example showing fault detection, its localization and reconfiguration of the control law to maintain asymptotic stability even in the presence of both permanent sensor faults and saturation on the control.     

Mean square exponential stability of stochastic neural networks with reaction–diffusion terms and delays

In this paper, some sufficient conditions ensuring mean square exponential stability of the equilibrium point of a class of stochastic neural networks with reaction–diffusion terms and time-varying delays are obtained. The conditions involving the effect of diffusion terms reduce the conservatism of the previous results. Finally, we give a numerical example to verify the effectiveness of our results.     

Exponential stability of fuzzy Cohen–Grossberg neural networks with time delays and impulsive effects

In this paper, we investigate a class of fuzzy Cohen-Grossberg neural networks with time delays and impulsive effects. By employing an inequality technique, we find sufficient conditions for the existence, uniqueness, global exponential stability of the equilibrium without using the M-matrix theory. An example is given to illustrate the effectiveness of the obtained results.     

New sufficient conditions for global asymptotic stability of Cohen–Grossberg neural networks with time-varying delays

In this paper, a class of Cohen–Grossberg neural networks with time-varying delays are considered. Without assuming the boundedness and monotonicity of activation functions, we establish new sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point for such delayed Cohen–Grossberg neural networks. Numerical examples are provided to show that the proposed criteria are less conservative than some results in the literature.