caa a22 4500 606205101 CHVBK 20210128101005.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00034-015-9978-7 doi (NATIONALLICENCE)springer-10.1007/s00034-015-9978-7 Steady-State Analysis of the Deficient Length Incremental LMS Adaptive Networks [Elektronische Daten] [Ghanbar Azarnia, Mohammad Tinati] The recently proposed distributed incremental least mean-square (DILMS) adaptive networks assume that the length of the adaptive filter at each node is equal to that of the unknown parameter; in other words, a sufficient length adaptive filter is assumed for each node. However, in many practical situations, the length of the employed adaptive filter at each node is less than that of the unknown parameter. In other words, at each node, a deficient length adaptive filter is employed. Since the analysis results for the sufficient length DILMS algorithm are not necessarily applicable to the deficient length case, so in this paper, we extend existing analysis to study the performance of the DILMS algorithm in this realistic case. More precisely, we derive a closed-form expression for the mean-square deviation (MSD) to explain the steady-state performance at each individual node. Simulation results support the theoretical analysis. Springer Science+Business Media New York, 2015 Adaptive networks nationallicence Deficient length nationallicence Distributed estimation nationallicence DILMS algorithm nationallicence Azarnia Ghanbar Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran aut Tinati Mohammad Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/9(2015-09-01), 2893-2910 0278-081X 34:9<2893 2015 34 34 https://doi.org/10.1007/s00034-015-9978-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-015-9978-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Azarnia Ghanbar Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran aut NATIONALLICENCE 700 1- Tinati Mohammad Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/9(2015-09-01), 2893-2910 0278-081X 34:9<2893 2015 34 34