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In this paper we propose the use of variable length adaptive filtering within the context of incremental learning for distributed networks. Algorithms for such incremental learning strategies must have low computational complexity and require minimal communication between nodes as compared to centralized networks. To match the dynamics of the data across the network we optimize the length of the adaptive...
A new incremental adaptive learning scheme based on the affine projection algorithm (APA), which is developed from Newton's method, is formulated for distributed networks to ameliorate the limited convergence properties of least-mean-square (LMS) type distributed adaptive filters with colored inputs. The simulation results verify that the proposed algorithm provides not only a faster convergence rate...
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