N. Gunasekaran

State estimation of T–S fuzzy delayed neural networks with Markovian jumping parameters using sampled-data control ☆

Abstract In this paper, we are concerned with the problem of state estimation of Takagi–Sugeno (T–S) fuzzy delayed neural networks with Markovian jumping parameters via sampled-data control. Based on the fuzzy-model-based control approach and linear matrix inequality (LMI) technique, several novel conditions are derived to guarantee the stability of the suggested system. A new class of Lyapunov functional, which contains integral terms, is constructed to derive delay-dependent stability criteria. Some characteristics of the sampling input delay are proposed based on the input delay approach. Numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results.

Sampled-data filtering of Takagi–Sugeno fuzzy neural networks with interval time-varying delays☆

Abstract This paper is concerned with sample-data filtering of T–S fuzzy neural networks with interval time-varying delays, which is formed by a fuzzy plant with time delay and a sampled-data fuzzy controller connected in a closed loop. A Takagi–Sugeno (T–S) fuzzy model is adopted for the neural networks and the sampled-data fuzzy controller is designed for a T–S fuzzy system. To develop the guaranteed cost control, a new stability condition of the closed-loop system is guaranteed in the continuous-time Lyapunov sense, and its sufficient conditions are formulated in terms of linear matrix inequalities. By using a descriptor representation, the sampled-data fuzzy control system with time delay can be reduced to ease the stability analysis, which effectively leads to a smaller number of LMI-stability conditions. Information of the membership functions of both the fuzzy plant model and fuzzy controller are considered, which allows arbitrary matrices to be introduced, to ease the satisfaction of the stability conditions. By a newly proposed inequality bounding technique, the fuzzy sampled-data filtering performance analysis is carried out such that the resultant neural networks is asymptotically stable. Numerical example and simulation result are given to illustrate the usefulness and effectiveness of the proposed theoretical results.

Robust stability of hopfield delayed neural networks via an augmented L-K functional

This paper focuses on the issue of robust stability of artificial delayed neural networks. A free-matrix-based inequality strategy is produced by presenting an arrangement of slack variables, which can be optimized by means of existing convex optimization algorithms. To reflect a large portion of the dynamical behaviors of the framework, uncertain parameters are considered. By constructing an augmented Lyapunov functional, sufficient conditions are derived to guarantee that the considered neural systems are completely stable. The conditions are presented in the form of as linear matrix inequalities (LMIs). Finally, numerical cases are given to show the suitability of the results presented.

Delay-dependent {\mathcal {H}}_\infty performance state estimation of static delayed neural networks using sampled-data control

In this paper, we consider the issue of delay-dependent

Dissipativity analysis of discrete-time Markovian jumping neural networks with time-varying delays

{\mathcal {H}}_\infty

A study on ⟨(Q,S,R)-γ⟩-dissipative synchronisation of coupled reaction–diffusion neural networks with time-varying delays

H∞ performance state estimation of static delayed neural networks using sampled-data control. A sensible Lyapunov–Krasovskii functional with triple and quadruplex integral terms is constructed. By using Jensen’s inequality, Wirtinger-based inequality, and reciprocally convex technique, the stability conditions are derived. Delay-dependent criterion is acquired under which the estimation error framework is asymptotically stable with an endorsed

Sampled-data state estimation for delayed Markovian jump neural networks based on passive theory

{\mathcal {H}}_\infty