Sequential nonlinear learning for distributed multiagent systems via extreme learning machines

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dc.citation.epage558en_US
dc.citation.issueNumber3en_US
dc.citation.spage546en_US
dc.citation.volumeNumber28en_US
dc.contributor.authorVanli, N. D.en_US
dc.contributor.authorSayin, M. O.en_US
dc.contributor.authorDelibalta, I.en_US
dc.contributor.authorKozat, S. S.en_US
dc.date.accessioned2018-04-12T11:02:53Z
dc.date.available2018-04-12T11:02:53Z
dc.date.issued2017en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe study online nonlinear learning over distributed multiagent systems, where each agent employs a single hidden layer feedforward neural network (SLFN) structure to sequentially minimize arbitrary loss functions. In particular, each agent trains its own SLFN using only the data that is revealed to itself. On the other hand, the aim of the multiagent system is to train the SLFN at each agent as well as the optimal centralized batch SLFN that has access to all the data, by exchanging information between neighboring agents. We address this problem by introducing a distributed subgradient-based extreme learning machine algorithm. The proposed algorithm provides guaranteed upper bounds on the performance of the SLFN at each agent and shows that each of these individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN. Our performance guarantees explicitly distinguish the effects of data-and network-dependent parameters on the convergence rate of the proposed algorithm. The experimental results illustrate that the proposed algorithm achieves the oracle performance significantly faster than the state-of-the-art methods in the machine learning and signal processing literature. Hence, the proposed method is highly appealing for the applications involving big data. © 2016 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:02:53Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017en
dc.identifier.doi10.1109/TNNLS.2016.2536649en_US
dc.identifier.issn2162-237X
dc.identifier.urihttp://hdl.handle.net/11693/37102
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TNNLS.2016.2536649en_US
dc.source.titleIEEE Transactions on Neural Networks and Learning Systemsen_US
dc.subjectDistributed systemsen_US
dc.subjectExtreme learning machine (ELM)en_US
dc.subjectMultiagent optimizationen_US
dc.subjectSequential learningen_US
dc.subjectSingle hidden layer feedforward neural networks (SLFNs)en_US
dc.titleSequential nonlinear learning for distributed multiagent systems via extreme learning machinesen_US
dc.typeArticleen_US

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