Tianping Chen

This article investigates distributed adaptive algorithms for intralayer synchronization of multiplex networks, both with and without pinning control. Two types of distributed adaptive algorithms are considered based on the parameters being adjusted: 1) node-based algorithms, which adapt the coupling strength of each node using the relative information from its neighborhood and itself, and 2) edge-based algorithms, which update the coupling weight of each edge based on the relative information between the two connected nodes. Using the Lyapunov function method, we prove that, under mild conditions on the uncoupled node dynamics, the proposed adaptive strategies guarantee intralayer synchronization for any multiplex network with strongly connected intralayer topologies.

In this article, we discuss synchronization in multiplex networks of different layers. Both the topologies and the uncoupled node dynamics in different layers are different. Novel sufficient criteria are derived for intralayer synchronization and interlayer quasisynchronization, in terms of the coupling matrices, the coupling strengths, and the intrinsic function of the uncoupled systems. We also investigate interlayer synchronization of multiplex networks with identical uncoupled node dynamics. Finally, we give some numerical examples to validate the effectiveness of these theoretical results.

In this letter, we propose high order layered complex networks. The synchronization is discussed in detail. The relations of synchronization, individual coupling matrices and the intrinsic function of the uncoupled system are given. As special cases, synchronization of monolayer networks and multiplex networks discussed in literature can be obtained.

This paper studies the stability of linearly coupled dynamical systems with feedback pinning algorithms. Here, both the coupling matrix and the set of pinned‐nodes are time‐varying, induced by stochastic processes. Event‐triggered rules are employed in both diffusion coupling and feedback pinning terms, which can reduce the actuation and communication loads. Two event‐triggered rules are proposed and it is proved that if the system with time‐average couplings and pinning gains is stable and the switching of coupling matrices and pinned nodes is sufficiently fast, the proposed event‐triggered strategies can stabilize the system. Moreover, Zeno behaviour can be excluded for all nodes. Numerical examples of networks of mobile agents are presented to illustrate the theoretical results.

For unsupervised classification of polarimetric synthetic aperture radar (PolSAR) images, it is a challenge task to determine an appropriate number of categories. Recently, a method called clustering by fast search and find of density peaks (CFSFDP) has provided a good solution. However, it requires to select the cluster centers by visual observation, which is not only less automated but also may be difficult to implement for some data sets. Focusing on this problem, this paper proposes a method of detecting cluster centers with the constant false alarm rate (CFAR) criterion, and also applies it to the unsupervised classification of PolSAR images. In our method, a variable called center-likelihood for each data point is constructed by multiplying its local density and distance that defined in the CFSFDP algorithm. Then, after estimating the probability density of the center-likelihood, the threshold to detect cluster centers is obtained with the CFAR criterion. The effectiveness of the proposed method is verified by the experimental results of two actual PolSAR images.