Hardware implementation of random neural networks with reinforcement learning

    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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