PROJECT TITLE :

Exponential Synchronization of Neural Networks With Discrete and Distributed Delays Under Time-Varying Sampling

ABSTRACT:

This paper investigates the matter of master-slave synchronization for neural networks with discrete and distributed delays below variable sampling with a known higher certain on the sampling intervals. An improved technique is proposed, which captures the characteristic of sampled-data systems. Some delay-dependent criteria are derived to make sure the exponential stability of the error systems, and thus the master systems synchronize with the slave systems. The desired sampled-information controller can be achieved by solving a group of linear matrix inequalitys, that rely upon the most sampling interval and therefore the decay rate. The obtained conditions not only have less conservatism however also have less call variables than existing results. Simulation results are given to point out the effectiveness and edges of the proposed methods.


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