  Diffusion adaptation is a powerful strategy for distributed estimation and
learning over networks. Motivated by the concept of combining adaptive filters,
this work proposes a combination framework that aggregates the operation of
multiple diffusion strategies for enhanced performance. By assigning a
combination coefficient to each node, and using an adaptation mechanism to
minimize the network error, we obtain a combined diffusion strategy that
benefits from the best characteristics of all component strategies
simultaneously in terms of excess-mean-square error (EMSE). Analyses of the
universality are provided to show the superior performance of affine
combination scheme and to characterize its behavior in the mean and mean-square
sense. Simulation results are presented to demonstrate the effectiveness of the
proposed strategies, as well as the accuracy of theoretical findings.
