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In this paper, a class of neural networks with time-varying delays are investigated for the first time using a periodically intermittent control technique. First, some new and useful stabilization criteria and synchronization conditions based on p-norm are derived by introducing multi-parameters and using the Lyapunov functional technique. For ∞-norm, using the analysis technique, some novel conditions ensuring exponential stability and synchronization are also obtained. It is worth noting that the methods used in this paper are totally different from the corresponding previous works and the obtained conditions are less conservative. Particularly, the traditional assumptions on control width and time delay are removed in this paper. Finally, some numerical simulations are given to verify the theoretical results.

In this paper, the exponential lag synchronization for a class of neural networks with discrete delays and distributed delays is studied via periodically intermittent control for the first time. Some novel and useful criteria are derived by using mathematical induction method and the analysis technique which are different from the methods employed in correspondingly previous works. Finally, some numerical simulations are given to demonstrate the effectiveness of the proposed control methods.

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In this paper, the consensus of fractional-order multiagent systems (FOMASs) is considered via sampled-data control over directed communication topology with the order 0 <; α <; 1. Two cases are considered. One is FOMASs without leader, and the other is FOMASs with a leader. For each case, by applying matrix theory and algebraic graph theory, some algebraic-type necessary and sufficient conditions based on the sampling period, the fractional-order, the coupling gain, and the structure of the network are established for achieving consensus of the system. Moreover, for the network with a dynamic leader, the sampling period, the coupling gain, and the spectrum of the Laplacian matrix are carefully devised, respectively. Finally, several simulation examples are employed to validate the effectiveness of the theoretical results.

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