Title

Hardware Implementation Of Random Neural Networks With Reinforcement Learning

Abstract

In this paper, we present, a hardware implementation of a random neural network (RNN) model. The RNN, introduced by Gelenbe. is a spiked neural network model that possesses several mathematical properties such as the existence and uniqueness of the solution, and convergence of the learning algorithm. In particular, we discuss the implementation details for an RNN which uses a reinforcement learning algorithm. We also illustrate an example where this circuit implementation is used as a building block in a recently proposed novel network routing protocol called cognitive packet networks (CPN). CPN does not employ a routing table instead it relies on the RNN with a. reinforcement algorithm to route probing packets. © Springer-Verlag Berlin Heidelberg 2006.

Publication Date

1-1-2006

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

4131 LNCS - I

Number of Pages

321-329

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/11840817_34

Socpus ID

33749827571 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33749827571

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