Adaptive exponential synchronization in mean square for Markovian jumping neutral-type coupled neural networks with time-varying delays by pinning control

Almost surely asymptotic synchronization for stochastic neural networks of neutral type with Markovian jumping parameters

“…Assumption 3 is weaker than those investigated in other works. 2,[9][10][11][12]15,16,19,24,25 In reality, if we take R 4 ≡ 0, then Assumption 3 is the same as in the works of Zhu and Cao, 9 Sun et al, 19 and Dai et al 25 Assumption 4. (t, 0, 0, 0, 0) ≡ 0.…”

“…On the other hand, owing to the fact that many practical systems can be described by neural networks of neutral type in the real world, the investigation on stability and stabilization of the neutral-type neural networks has attracted many researchers' attention, some interesting results have been published in the literature (see other works [20][21][22][23][24][25][26][27] and the references therein). For example, the work of Park and Kwon 20 studied the delay-dependent stability criterion for a class of neural networks of neutral type with interval time-varying delays by the Lyapunov stability theory and linear matrix inequality approach.…”

“…A Markov jump neural network undergoes conversion from one mode to another, between a finite and denumerable number of possible modes. Since the study of the Markovian jumping neural networks with control techniques is given more importance over the past few years, a great deal of meaningful results on analysis and design of Markovian jumping systems have been available in the literature, 25,[28][29][30][31][32][33][34][35][36][37][38][39] and its applications can be found in many practical systems, such as networked control systems, 40 electric power systems, fault-tolerant systems, 41 target tracking systems, 42 and so on. Therefore, it is of significance to study the almost surely asymptotic synchronization problem for stochastic neutral-type neural networks with both Markovian jumping parameters and time-varying delays.…”

Stabilization of Discrete-Time Markovian Jump Systems by a Partially Mode-Unmatched Fault-Tolerant Controller

“…It also has been widely studied in the field of industrial process control, space flight, medical treatment, electric power and economy. During the past few decades, many important results based on kinds of systems have emerged, such as stability analysis [1][2][3][4][5][6], stabilization [7][8][9][10], delay case [11][12][13], output control [14,15], H ∞ control [16][17][18][19] and filtering [20][21][22], robust control [23][24][25], sliding control [26], state estimation [27], fault detection [28], synchronization [29,30], and so on.…”

Stability and synchronization analysis of inertial memristive neural networks with time delays

“…Synchronization of coupled inertial neural networks means that multiple neural networks can achieve a common trajectory, such as a common equilibrium, limit cycle or chaotic trajectory. The authors in Dai et al (2016) have analyzed, the problem of neutral-type coupled neural networks with Markovian switching parameters by placing the adaptive controllers to part of nodes, and the sufficient conditions for exponential synchronization are drawn with the help of Lyapunov stability theory, stochastic analysis and matrix theory. Outer synchronization of partially coupled dynamical networks via pinning impulsive controller has been discussed in Lu et al (2015).…”