Title

Hardware implementation of random neural networks with reinforcement learning

Authors

Authors

T. Kocak

Comments

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Keywords

Computer Science, Artificial Intelligence; Computer Science, Theory &; Methods

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.

Journal Title

Artificial Neural Networks - Icann 2006, Pt 1

Volume

4131

Publication Date

1-1-2006

Document Type

Article

Language

English

First Page

321

Last Page

329

WOS Identifier

WOS:000241472100034

ISSN

0302-9743; 3-540-38625-4

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