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This work examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gradient vector. The selection of which coordinates to use varies randomly from iteration to iteration and from agent to agent across the network....
The proportionate normalized least-mean-square (PNLMS) algorithm with individual activation factors (IAFPNLMS) converges fast when the echo path is highly sparse, and has been used in system identification. Unfortunately, it suffers from slow convergence speed after the fast initial process. To solve the problem, in this paper, the idea of mu-law PNLMS (MPNLMS) algorithm is introduced into the IAFPNLMS...
A convex combination LMS (least mean square) algorithm based on Krylov subspace transform is proposed in this paper. In this approach, impulse response of the unknown system is firstly transformed into Krylov subspace, in which the system structure is changed into sparse. Then an improved proportionate normalized LMS (IPNLMS) algorithm and a variable tap-length normalized LMS (VTNLMS) algorithm are...
In this paper a Krylov subspace transform domain lease mean square (LMS) algorithm is proposed. The unknown system can be sparse after Krylov subspace transform, thus a much smaller tap length can be used for the update of adaptive filer coefficients in transform domain, which results in a significant improvement of convergence rate. The small tap length in transform domain can be found by using variable...
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 variable tap-length adaptive algorithm which exploits both second and fourth order statistics is proposed in this paper. In this algorithm, the tap-length of the adaptive filter is varying rather than fixed, and controlled by fourth order statistics, whereas the coefficient update retains a conventional least mean square (LMS) form. As will be seen in the simulation results, the proposed algorithm...
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