Robust observer for discrete-time Markovian jumping neural networks with mixed mode-dependent delays

Abstract

The robust observer problem is considered in this paper for a class of discrete-time neural networks with Markovian jumping parameters and modedependent time delays which are in both discretetime form and finite distributed form. The neural network switches from one mode to another controlled by a Markov chain with known transition probability. Time-delays considered in this paper are modedependent which may reflect a more realistic version of the neural network. By using the Lyapunov functional method and the techniques of linear matrix inequalities (LMIs), sufficient conditions are established in terms of LMIs that ensure the existence of the robust observer. The obtained conditions are easy to be verified via the LMI toolbox. An example is presented to show the effectiveness of the obtained results.

Hopf bifurcation and spatio-temporal patterns in a hierarchical network with delays andZ2×Znsymmetry

“…A variety of research carried on neural network models with delays has shown that delays can lead to interesting dynamics in various ways. For more details, we refer to [3][4][5][6][7][8][9][10][11][12], and references therein. In addition, due to the complexity of dynamics of neural networks, some recent works (e.g., [13][14][15][16][17][18]) have focused on networks with the same time delay, the small scale or simple architectures.…”

State Estimation for Discrete-Time Fuzzy Cellular Neural Networks with Mixed Time Delays

“…In reality, however, discrete-time neural networks (DNNs) become more important than their continuous-time counterparts when implementing the neural networks in a digital way. Therefore, it is necessary to study the dynamics of DNNs and many results have been obtained during the past years [10][11][12][13][14][15][16][17]. By using the average dwell time approach and the discontinuous piecewise Lyapunov function technique, Zhang and Yu [16] studied passivity analysis problem for a class of discretetime switched neural networks with various activation functions and mixed time delays.…”

Mean square robust stability of stochastic switched discrete-time systems with convex polytopic uncertainties

“…Switched systems have drawn a great deal of attention in recent years, see [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] and references therein. The motivation for studying switched systems comes partly from the fact that switched systems and switched multi-controller systems have numerous applications in control of mechanical systems, process control, automotive industry, power systems, aircraft and traffic control, and many other fields.…”