Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3143
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dc.contributor.authorGupta, Saurav-
dc.contributor.authorSahoo, Ajit Kumar-
dc.contributor.authorSahoo, Upendra Kumar-
dc.date.accessioned2018-12-31T05:08:36Z-
dc.date.available2018-12-31T05:08:36Z-
dc.date.issued2018-12-
dc.identifier.citation15th IEEE India Council International conference ( INDICON 2018 ), Coimbatore, India, 16-18 December, 2018en_US
dc.identifier.urihttp://hdl.handle.net/2080/3143-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractThe distributed estimation over wireless sensor networks (WSNs), as opposed to least-squares and fusion-center based estimations, is proficient to work with real-time applications. In this paper, a block-structured Wiener model is identified in a distributed fashion by minimizing the least-squares cost function on prediction error. As the block-structured Wiener model can approximate a large class of nonlinear systems with a small number of characteristics parameters hence makes it more suitable to work with. The global minimization task is reformed into several constrained subtasks in a manner that each node in WSN can obtain the parameters of interest locally. Each node in the network has the ability to combine its local estimates with the single-hop neighbors’ estimates to obtain the global parameters of interest. The optimization of the reformulated cost is accomplished using a powerful distributed method called alternating direction method of multipliers. Simulations are carried on an infinite-order nonlinear system under the impact of observation noise. The obtained results are juxtaposed to the results of non-cooperative algorithm to show the effectiveness and superiority of the proposed algorithm.en_US
dc.subjectWSNen_US
dc.subjectNonlinear systemsen_US
dc.subjectDistributeden_US
dc.subjectBasis functionsen_US
dc.subjectADMMen_US
dc.subjectFusion-centeren_US
dc.titleADMM-Based Distributed Recursive Identification of Wiener Nonlinear Systems Using WSNsen_US
dc.typeArticleen_US
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