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Auto-tuning procedures for distributed nonparametric regression algorithms
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Department of Information Engineering, University of Padova.
Department of Information Engineering, University of Padova.
2015 (English)In: 2015 European Control Conference (ECC): Linz, 15-17 July 2015, Piscataway, NJ: IEEE Communications Society, 2015, 640-647 p.Conference paper (Refereed)
Abstract [en]

We propose a distributed regression algorithm with the capability of automatically calibrating its parameters during its on-line functioning. The estimation procedure corresponds to a Regularization Network, i.e., the structural form of the estimator is a linear combination of basis functions which coefficients are computed by solving a linear system. The automatic tuning strategy instead constructs and then exploits opportune bounds on the distance between the distributed estimation results and the unknown centralized optimal estimate that would be computed processing the whole dataset at once. By numerical simulations we show how the proposed procedure allows the sensor networks to effectively self-tune the parameters of the distributed regression scheme by simple consensus strategies.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015. 640-647 p.
Research subject
Control Engineering; Enabling ICT (AERI); Intelligent industrial processes (AERI)
Identifiers
URN: urn:nbn:se:ltu:diva-38024DOI: 10.1109/ECC.2015.7330614Local ID: c4531cb2-d8c7-42e2-bc32-55ed2ca0a897OAI: oai:DiVA.org:ltu-38024DiVA: diva2:1011523
Conference
European Control Conference : 15/07/2015 - 17/07/2015
Note
Godkänd; 2015; 20150327 (damvar)Available from: 2016-10-03 Created: 2016-10-03Bibliographically approved

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