This paper investigates the generalized synchronization of chaotic dynamics in resistive capacitive inductance (RCL)-shunted Josephson junctions with uncertain parameters.Based on Lyapunove stability theory and adaptive control method, unified nonlinear feedback controller and the parameter update laws are pesented .Numerical simulation illustrate that the system can realize generalized synchronization by different scaling factors .

This paper deals with the function projective synchronization of two complex dynamic networks with unknown sector nonlinear input, multiple time-varying delay couplings, model uncertainty, and external interferences. Based on Lyapunov stability theory and inequality transformation method, the robust adaptive synchronization controller is designed, by which the drive and response systems can achieve synchronization according to the function scaling factor. Different from some existing studies on nonlinear system with sector nonlinear input, this paper studies the synchronization of two complex dynamic networks when the boundary of sector nonlinear input is unknown. The controller does not include the boundary value of the sector nonlinear input and the time delay term, so it is more practical and relatively easy to implement. The corresponding simulation examples demonstrate the effectiveness of the proposed scheme.

The wireless sensor networks (WSNs) require an optimal selection of control nodes for improving the operational performance of the overall network. The data are increasing day by day, and it is difficult to a handle huge amount of data. For speedy transmission of data, it is mandatory to deploy sophisticated methods for improving the operations of WSNs. There are many methods proposed by the researchers to improve the operations of WSNs, but the data are increasing and more methods are needed to be explored to handle the operations of WSNs to smoothly handle a huge amount of data. To cater to this need, this research is proposing a method of selecting optimal control nodes for WSNs based on the C-means clustering algorithm (CCA). The CCA is improved by the weighting mechanism in the cluster, and the remaining energy of the node is taken into account. If the node energy is more as compared to the average energy in the cluster in each round, it will have the chance to serve as the cluster head node (CHN) and the adaptive assignment of CHN is made according to the generated cluster size by WSN. Every node possesses the probability of becoming a CHN to save the energy utilization of the node and to obtain the optimal control for node selection in WSN. The experimental results reveal that the coverage rate of WSN is improved after applying the proposed method. The network energy utilization is optimized, which effectively prolongs the lifetime of WSN and improves the overall network output including throughput, energy consumption rate, and data transmission rate.