Robust stability criteria for interval Cohen–Grossberg neural networks with time varying delay

Novel stability criteria of Cohen–Grossberg neural networks with time‐varying delays

“…And, the exponential convergence rate index /2 can be obtained from (7) and (16). The proof is completed.…”

New global exponential stability result to a general Cohen–Grossberg neural networks with multiple delays

“…When system (1.2) reduces to the one-dimensional Cohen-Grossberg neural networks, our result on global exponential stability is different from the existing results. In our result, the hypothesis for differentiability in [7,8,22,25] on the behaved functions is removed and the hypotheses for boundedness in [2, 3, 8-12, 16, 18, 21-25] and monotonicity in [7,15,22,23,25,26] on the activation functions are removed. We just require that the behaved functions satisfy two inequalities and activation functions are globally Lipschitz continuous.…”

Further results for robust stability of cellular neural networks with linear fractional uncertainty

“…It is easy to see that all the stability results in [12,[15][16][17][18][19][24][25][26][27][28][29][30]34,36,42] are delay-independent. Further, all the stability results in [13,15,23,29] are independent of the derivative of time delay.…”

“…Further, it is easy to verify that, for any s 1 P 0 and g, none of the criteria given in [7,12,[15][16][17][18][19][23][24][25][26][27][28][29][30][32][33][34]36,42] can conclude whether the model is asymptotic stable or not. More computational results are shown in Table 1, which present the comparison data between our new results and the recent ones [6,22,38,41], where ''À" means that the result is not applicable to the corresponding case, and ''unknown g" means that g can be arbitrary value or sðtÞ can be not differentiable.…”

“…By applying nonsmooth analysis techniques and matrix norms, Qi [12] brought out several delay-independent criteria for the existence, uniqueness and robust stability; By applying homeomorphism and Linear matrix inequality (LMI) techniques, Wang et al [25] presented several delay-independent criteria for the existence, uniqueness and robust stability. For interval uncertain neural network with multiple time-varying delay, by using matrix decomposition method, LMI techniques, M-matrix theory, matrix norms and Halanay inequality techniques, Zhang et al [27,34,36] established several delay-independent criteria for the uniqueness and robust stability. For uncertain neural network with multiple time-varying delays and continuously distributed delays, by means of state transmission matrix, Wang et al [26] introduced a delay-independent robust stability criterion.…”