26,389 research outputs found
Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View
Distributed adaptive filtering has been considered as an effective approach
for data processing and estimation over distributed networks. Most existing
distributed adaptive filtering algorithms focus on designing different
information diffusion rules, regardless of the nature evolutionary
characteristic of a distributed network. In this paper, we study the adaptive
network from the game theoretic perspective and formulate the distributed
adaptive filtering problem as a graphical evolutionary game. With the proposed
formulation, the nodes in the network are regarded as players and the local
combiner of estimation information from different neighbors is regarded as
different strategies selection. We show that this graphical evolutionary game
framework is very general and can unify the existing adaptive network
algorithms. Based on this framework, as examples, we further propose two
error-aware adaptive filtering algorithms. Moreover, we use graphical
evolutionary game theory to analyze the information diffusion process over the
adaptive networks and evolutionarily stable strategy of the system. Finally,
simulation results are shown to verify the effectiveness of our analysis and
proposed methods.Comment: Accepted by IEEE Transactions on Signal Processin
A Novel Family of Adaptive Filtering Algorithms Based on The Logarithmic Cost
We introduce a novel family of adaptive filtering algorithms based on a
relative logarithmic cost. The new family intrinsically combines the higher and
lower order measures of the error into a single continuous update based on the
error amount. We introduce important members of this family of algorithms such
as the least mean logarithmic square (LMLS) and least logarithmic absolute
difference (LLAD) algorithms that improve the convergence performance of the
conventional algorithms. However, our approach and analysis are generic such
that they cover other well-known cost functions as described in the paper. The
LMLS algorithm achieves comparable convergence performance with the least mean
fourth (LMF) algorithm and extends the stability bound on the step size. The
LLAD and least mean square (LMS) algorithms demonstrate similar convergence
performance in impulse-free noise environments while the LLAD algorithm is
robust against impulsive interferences and outperforms the sign algorithm (SA).
We analyze the transient, steady state and tracking performance of the
introduced algorithms and demonstrate the match of the theoretical analyzes and
simulation results. We show the extended stability bound of the LMLS algorithm
and analyze the robustness of the LLAD algorithm against impulsive
interferences. Finally, we demonstrate the performance of our algorithms in
different scenarios through numerical examples.Comment: Submitted to IEEE Transactions on Signal Processin
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