access icon free Variable step-size distributed incremental normalised LMS algorithm

A variable step-size distributed incremental normalised least mean square (DINLMS) algorithm is proposed, in which the time-varying step-size of each node in the distributed network is obtained by minimising a posterior estimation error at that node. It overcomes the drawback that fast convergence rate with high steady-state misalignment or low steady-state misalignment with slow convergence rate, which always exists in the traditional DINLMS. The simulation results show that the proposed algorithm could achieve a better performance.

Inspec keywords: least squares approximations; maximum likelihood estimation

Other keywords: least mean square algorithm; fast convergence rate; DINLMS algorithm; low steady-state misalignment; distributed network; a posterior estimation error minimization; high steady-state misalignment; variable step-size distributed incremental normalised LMS algorithm

Subjects: Numerical analysis; Interpolation and function approximation (numerical analysis); Numerical approximation and analysis; Probability theory, stochastic processes, and statistics; Other topics in statistics; Interpolation and function approximation (numerical analysis); Other topics in statistics; Statistics

References

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      • 1. Lopes, C.G., Sayed, A.H.: ‘Distributed adaptive incremental strategies: formulation and performance analysis’. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Toulouse, France, May 2006, pp. 584587.
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2015.3882
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