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Active cyber defense is one important defensive method for combating cyber attacks. Unlike traditional defensive methods such as firewall-based filtering and anti-malware tools, active cyber defense is based on spreading “white” or “benign” worms to combat against the attackers’ malwares (i.e., malicious worms) that also spread over the network. In this(More)
This paper mainly investigates consensus problem with a pull-based event-triggered feedback control. For each agent, the diffusion coupling feedbacks are based on the states of its in-neighbors at its latest triggering time, and the next triggering time of this agent is determined by its in-neighbors' information. The general directed topologies, including(More)
The consensus problem for multi-agent systems with quantized communication or sensing is considered. Centralized and distributed self-triggered rules are proposed to reduce the overall need of communication and system updates. It is proved that these self-triggered rules realize consensus exponentially if the network topologies have a spanning tree and the(More)
This work presents chaos synchronization between the uncertain chaotic Lorenz system and the certain chaotic third-order Cellular Neural Networks (CNN) via adaptive control. Based on Lyapunov stability theory, an adaptive controller and parameters estimation update laws for unknown parameters are given such that the two different chaotic systems are to be(More)
In this paper, we investigate stability of a class of analytic neural networks with the synaptic feedback via event-triggered rules. This model is general and include Hopfield neural network as a special case. These event-trigger rules can efficiently reduces loads of computation and information transmission at synapses of the neurons. The synaptic feedback(More)
In this paper, event-triggered algorithm and self-triggered algorithms are proposed to establish the formation with connectivity preservation for multi-agent systems. Each agent only needs to update its control input by sensing the relative state information and to broadcast its triggering information to its neighbors at its own triggering times, and to(More)
This paper investigates the convergence of Hop-field neural networks with an event-triggered rule to reduce the frequency of the neuron output feedbacks. The output feedback of each neuron is based on the outputs of its neighbours at its latest triggering time and the next triggering time of this neuron is determined by a criterion based on its neighborhood(More)
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