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This study aims at deriving a less conservative delay-dependent criterion for the robust H ∞ stability analysis of discrete-time systems with interval time-varying state delay. To this end, an appropriate Lyapunov-Krasovskii functional containing triple summation terms is established and a relaxed inequality approach is proposed to address the induced double summation inequality and to exploits novel relaxation variables for null sum terms. Three numerical examples are provided to illustrate the effectiveness of the derived stability criteria.

In this paper, the problem of stability analysis for neural networks with time-varying delays is considered. By constructing a new augmented Lyapunov-Krasovskii's functional and some novel analysis techniques, improved delaydependent criteria for checking the stability of the neural networks are established. The proposed criteria are presented in terms of linear matrix inequalities (LMIs) which can be easily solved and checked by various convex optimization algorithms. Two numerical examples are included to show the superiority of our results.

Pretreatment measurement of SCCA and urine polyamine levels may help in predicting lymph node metastases in women with early stage cervical carcinoma.