期刊
MATHEMATICS
卷 9, 期 19, 页码 -出版社
MDPI
DOI: 10.3390/math9192436
关键词
complex networks; discrete-time impulsive systems; impulsive control; neural networks
类别
This paper presents an impulsive pinning control algorithm for discrete-time complex networks, using a linear algebra approach and neural network for identification to synthesize a learning control law. A numerical simulation is included to illustrate the system behavior under the developed controller.
In this paper, we present an impulsive pinning control algorithm for discrete-time complex networks with different node dynamics, using a linear algebra approach and a neural network as an identifier, to synthesize a learning control law. The model of the complex network used in the analysis has unknown node self-dynamics, linear connections between nodes, where the impulsive dynamics add feedback control input only to the pinned nodes. The proposed controller consists of the linearization for the node dynamics and a reorder of the resulting quadratic Lyapunov function using the Rayleigh quotient. The learning part of the control is done with a discrete-time recurrent high order neural network used for identification of the pinned nodes, which is trained using an extended Kalman filter algorithm. A numerical simulation is included in order to illustrate the behavior of the system under the developed controller. For this simulation, a 20-node complex network with 5 different node dynamics is used. The node dynamics consists of discretized versions of well-known continuous chaotic attractors.
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