New Global Asymptotic Stability for Neural Networks with Time-Varying Discrete and Distributed Delays

Abstract

In this paper, the global asymptotic stability problem is considered for a class of neural networks with time-varying discrete and distributed delays. Based on the Lyapunov functional method, and by using the new technique for estimating the upper bound of the derivative of Lyapunov functional, the novel asymptotic stability criterion is derived in terms of linear matrix inequalities(LMIs). Two numerical examples are presented to show the less conservativeness of the proposed method.

Measuring individual learning performance in group work from a knowledge integration perspective

“…Moreover, we are considering the inclusion of a new factor in the scoring function accounting for the evolution of the student along the assignment. Approaches such as those presented in [10,16,30] provide interesting mechanisms to address these issues.…”

On the excitability of a class of positive continuous time-delay systems

“…(29), which implies that y(2h + )b0 for any x 0 40 and any function jðtÞ 2 R n þ for tA[Àh, 0). The ''necessity parts'' of (iii) for weak transparency and transparency follow directly by contradiction.…”

“…For instance, the robust decentralized closed-loop stabilization of interconnected systems with certain nonlinearities has been investigated in [28] by means of a fuzzy controller. On the other hand, the robust stabilization of cellular neural networks involving both discrete and distributed delays has been studied in [29]. The state-feedback stabilization of systems possessing delays through a memoryless controller and the optimal filtering under multiple state and observation delays have been investigated in [30,31], respectively.…”

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