state estimation of discrete-time markov jump neural networks with general transition probabilities and output quantization

Finite-Time MixedH∞/Passivity for Neural Networks With Mixed Interval Time-Varying Delays Using the Multiple Integral Lyapunov-Krasovskii Functional

“…For given scalars τ = 0.5, c 1 = 1.12, ρ = 0.1, T = 30, α = 0.1 and V is identity matrix. Solving LMIs (36) in Corollary 1, we obtain c 2 = 5.37.…”

Event-triggered H∞ filtering for discrete-time Markov jump delayed neural networks with quantizations

“…Case 1: σ 1 = σ 2 and Case 2: σ 1 = σ 2 . For Case 1, the event-triggered parameter σ 1 = σ 2 = 0.2, when d m = 1, d M = 4, the maximum delay in communication network τ M = 5, and = 1, γ = 3, by using the LMI toolbox of Matlab, it is easy to obtain the following matrices: For Case 2, the event-triggered parameter σ 1 = 0.2, σ 2 = 0.1, when d m = 1, d M = 4, the maximum delay in communication network τ M = 5, and = 1, γ = 3, by using the LMI toolbox of Matlab, it is easy to obtain the following matrices: Remark IV.1: Although some feasible results have been developed in the existing literature to deal with H ∞ filter (estimation) for neural networks with quantizations (Sasirekha et al, 2017;Zhuang et al, 2016), they are difficult to be applied directly to deal with the case that exist in the event-triggered scheme. Comparing the existing literature and simulation results, we can easily conclude that the event-triggered mechanism can reduce the use of network bandwidth effectively.…”

“…The problem of robust H ∞ estimation for a class of MJNNs with transmission delay, measurement quantization and data packet dropout is studied in Zhuang et al (2016). In Sasirekha, Rakkiyappan, Cao, Wan, and Alsaedi (2017), H ∞ state estimation of discrete-time Markov jump neural networks with general transition probabilities and output quantization is described. The effect of the quantization on the networked control systems is greater than the traditional control systems.…”

Non-fragile mixed H-infinity and passivity control for neural networks with successive time-varying delay components

“…Similarly, admissible upper bounds of σ 12 for different values of σ 22 with σ 11 = 0, σ 21 = 0, µ 1 = 0.7, µ 2 = 0.1, 0.2 are described in Table 2. For system (33) with the above parameters, Fig. 2 shows, for the state responses z(t), when σ 12 = 0.8, σ 22 = 2.7651, and the initial condition (−0.2, 0.2) T .…”

“…It is the aim of this theory to design the controller such that the closed-loop system is internally stable and its H ∞ norm of the transfer function between the controlled output and the disturbances will not exceed a given H ∞ performance level γ. Hence, there has been increasing interest in the problem of H ∞ control of dynamical systems because of their useful applications in robust control, image processing, especially in classification of patterns, associative memories and other areas [4,8,14,33,36,40,47,58]. Therefore, in general, it is important both theoretically and practically to studied the stability criteria of the dynamical systems.…”