Passivity Analysis of Non-autonomous Discrete-Time Inertial Neural Networks with Time-Varying Delays

Robust passivity analysis for uncertain stochastic switched inertial neural networks with time‐varying delay under a new state‐dependent switching law

“…Remark The passivity results of some types of INNs were given in [36–42, 52] without using the SDS method. However, the passivity results of SSINNs () and robust passivity results of SSINNs () are considered in this paper using the SDS method; therefore, our results are significant over the results obtained in the previous works [36–42, 52]. Moreover, the passivity results of SSNNs without inertial terms are presented in [23, 29, 45].…”

“…Also, the general case of time delay, namely, the time‐varying case, is considered in this paper, in which no restriction on the derivative of time delay is imposed, which is also a significant feature of this paper. The main contributions to this paper are as follows: Most of the existing passivity analyses [21–35] are concerned with the first‐order derivative of NNs, and only a few results are obtained on the second‐order derivative of various types of NNs [36–42]. But in this paper, we discuss a novel SDS approach and robust passivity analysis for SSINNs based on a suitable variable transformation technique.…”

“…Strong biological foundations are presented in [36] for adding an inertial term to NNs. Nevertheless, only a few works have been published on the passivity of inertial neural networks (INNs); see [37][38][39][40][41][42].…”

“…(i) Most of the existing passivity analyses [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] are concerned with the first-order derivative of NNs, and only a few results are obtained on the second-order derivative of various types of NNs [36][37][38][39][40][41][42]. But in this paper, we discuss a novel SDS approach and robust passivity analysis for SSINNs based on a suitable variable transformation technique.…”

Passivity analysis of multiple time-varying time delayed complex variable neural networks in finite-time

“…The authors of [32] used the Lyapunov-Krasovskii functional approach and weighting matrices to study passivity analysis of stochastic time-delay neural networks. In addition, the subject of passivity analysis for various neural networks has received a lot of attention [33][34][35][36][37][38][39][40][41][42]. To the best of authors knowledge, so far, no result on the finite-time passivity for complex valued neural network systems with time varying delay has been reported.…”