A distributed adaptive clustering strategy algorithm

摘要

#$%^&*AU2019101412A420200102.pdf#####Abstract: A distributed adaptive clustering algorithm over dynamic multi-task networks, the algorithm mainly comprising two processes including normal task adaptation and the same task clustering. In the process of abnormal task adaptation, the algorithm use the task anomaly detection based on the noncooperative least mean square (NC-LMS) algorithm; in the same task clustering process, the algorithm use the task based on the diffusion maximum entropy criterion (D-MCC) algorithm. Switch detection. In multi-task networks, a series of scenarios, such as dynamic network, timevarying tasks and non-stationary (Gaussian and pulse interference). The algorithm also discuss optimization schemes to design the NC-LMS and D-MCC weights and examine the estimate performance and clustering effects of the proposed algorithm by simulation results. Compared to the prior art the present invention provides an effective distributed adaptive clustering strategy to solve distributed adaptive estimation over dynamic multi-task networks. Besides, since nodes in the network are constrained by communication power consumption and external interference in a nonstationary environment, the objective pursued by the node is prone to change or abnormality.1/3 Fig.1 120 100 14 60 40- 1 20 a 0 20 40 60 80 100 120 x-coord nate Fig.2 110 1 90 808 x7o70 50 40 30 7 20 10 1 0 20 40 60 80 100 120 x-coord inate