Abstract
In this paper, the optimization methods in designing a finite-time average consensus protocol for multi-agent systems or wireless sensor network are taken into account. As we all know, the purpose of the average consensus protocol is that all agents reach the final common value, which is the average of the initial values. In order to run a consensus protocol, there are 2 main steps: self-configuration step, and execution step. In the self-configuration step, the consensus protocol is designed and uploaded to each agent of the system so that the final average value is achieved in the minimal execution time. The proposed optimization method is based on learning and training methods applied for neural network.
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