Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines

We 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...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2017-03, Vol.28 (3), p.546-558
Hauptverfasser:	Vanli, Nuri Denizcan, Sayin, Muhammed O., Delibalta, Ibrahim, Kozat, Suleyman Serdar
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Convergence Cost function Data management Data processing Distance learning Distributed systems extreme learning machine (ELM) Learning algorithms Learning systems Machine learning Machine learning algorithms multiagent optimization Multiagent systems Neural networks sequential learning Signal processing Signal processing algorithms single hidden layer feedforward neural networks (SLFNs) State of the art Upper bounds
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