TY - JOUR
ID - 888
TI - Distributed Incremental Least Mean-Square for Parameter Estimation using Heterogeneous Adaptive Networks in Unreliable Measurements
JO - Journal of AI and Data Mining
JA - JADM
LA - en
SN - 2322-5211
AU - Farhid, M.
AU - Shamsi, M.
AU - Sedaaghi, M. H.
AD - Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
Y1 - 2017
PY - 2017
VL - 5
IS - 2
SP - 285
EP - 291
KW - Adaptive networks
KW - distributed estimation
KW - Least mean-square (LMS)
KW - informed nodes
KW - mean square deviation (MSD)
DO - 10.22044/jadm.2017.888
N2 - Adaptive networks include a set of nodes with adaptation and learning abilities for modeling various types of self-organized and complex activities encountered in the real world. This paper presents the effect of heterogeneously distributed incremental LMS algorithm with ideal links on the quality of unknown parameter estimation. In heterogeneous adaptive networks, a fraction of the nodes, defined based on previously calculated signal to noise ratio (SNR), is assumed to be the informed nodes that collect data and perform in-network processing, while the remaining nodes are assumed to be uninformed and only participate in the processing tasks. As our simulation results show, the proposed algorithm not only considerably improves the performance of the Distributed Incremental LMS algorithm in a same condition, but also proves a good accuracy of estimation in cases where some of the nodes make unreliable observations (noisy nodes). Also studied is the application of the same algorithm on the cases where node failure happens
UR - http://jad.shahroodut.ac.ir/article_888.html
L1 - http://jad.shahroodut.ac.ir/article_888_6852d57632da3aac42735715b0c7bc65.pdf
ER -