title: Scale-Limited Lagrange Stability and Finite-Time Synchronization for Memristive Recurrent Neural Networks on Time Scales.
abstract: The existed results of Lagrange stability and finite-time synchronization for memristive recurrent neural networks (MRNNs) are scale-free on time evolvement, and some restrictions appear naturally. In this paper, two novel scale-limited comparison principles are established by means of inequality techniques and induction principle on time scales. Then the results concerning Lagrange stability and global finite-time synchronization of MRNNs on time scales are obtained. Scaled-limited Lagrange stability criteria are derived, in detail, via nonsmooth analysis and theory of time scales. Moreover, novel criteria for achieving the global finite-time synchronization are acquired. In addition, the derived method can also be used to study global finite-time stabilization. The proposed results extend or improve the existed ones in the literatures. Two numerical examples are chosen to show the effectiveness of the obtained results.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Xiao
authors.forename: Zhigang
authors.initials: Z
authors.lastname: Zeng
mesh_terms: D003491:Cybernetics; D055615:Electrical Equipment and Supplies; D007257:Information Theory; D015233:Models, Statistical; D016571:Neural Networks, Computer; D013997:Time Factors