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ECHO STATE QUEUEING NETWORKS: A COMBINATION OF RESERVOIR COMPUTING AND RANDOM NEURAL NETWORKS

Published online by Cambridge University Press:  17 May 2017

Sebastián Basterrech Open the ORCID record for Sebastián Basterrech [Opens in a new window]  and
Gerardo Rubino
Sebastián Basterrech
Affiliation:
Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University, Karlovo náměstí 13, 121 35 Prague 2, Praha, Czech Republic E-mail: [email protected]
Gerardo Rubino
Affiliation:
Inria Rennes – Bretagne Atlantique, Campus de Beaulieu, 35042 Rennes Cedex, France E-mail: [email protected]
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Abstract

This paper deals with two ideas appeared during the last developing phase in Artificial Intelligence: Reservoir Computing (RC) and Random Neural Networks. Both have been very successful in many applications. We propose a new model belonging to the first class, taking the structure of the second for its dynamics. The new model is called Echo State Queuing Network. The paper positions the model in the global Machine Learning area, and provides examples of its use and performances. We show on largely used benchmarks that it is a very accurate tool, and we illustrate how it compares with standard RC models.

Keywords

applied probabilityqueueing theoryoperations researchcomputational probability
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 31 , Special Issue 4: G-Networks and their Applications , October 2017 , pp. 457 - 476
Copyright
Copyright © Cambridge University Press 2017 

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