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Birkhäuser

Code Recognition and Set Selection with Neural Networks

  • Book
  • © 1991

Overview

Editors:
  1. Clark Jeffries

    1. Department of Mathematical Sciences, Clemson University, Clemson, USA

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Part of the book series: Mathematical Modeling (MMO, volume 7)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-viii
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  2. The Neural Network Approach to Problem Solving

    • Clark Jeffries
    Pages 1-8

  3. Neural Networks as Dynamical Systems

    • Clark Jeffries
    Pages 9-32

  4. Hypergraphs and Neural Networks

    • Clark Jeffries
    Pages 33-52

  5. The Memory Model

    • Clark Jeffries
    Pages 53-81

  6. Code Recognition, Digital Communications, and General Recognition

    • Clark Jeffries
    Pages 82-101

  7. Memory Models with Limit Cycles as Attractors

    • Clark Jeffries
    Pages 102-117

  8. Solving Operations Research Problems with Neural Networks

    • Clark Jeffries
    Pages 118-128

  9. Back Matter

    Pages 129-166
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Keywords

About this book

In mathematics there are limits, speed limits of a sort, on how many computational steps are required to solve certain problems. The theory of computational complexity deals with such limits, in particular whether solving an n-dimensional version of a particular problem can be accomplished with, say, 2 n n steps or will inevitably require 2 steps. Such a bound, together with a physical limit on computational speed in a machine, could be used to establish a speed limit for a particular problem. But there is nothing in the theory of computational complexity which precludes the possibility of constructing analog devices that solve such problems faster. It is a general goal of neural network researchers to circumvent the inherent limits of serial computation. As an example of an n-dimensional problem, one might wish to order n distinct numbers between 0 and 1. One could simply write all n! ways to list the numbers and test each list for the increasing property. There are much more efficient ways to solve this problem; in fact, the number of steps required by the best sorting algorithm applied to this problem is proportional to n In n .

Editors and Affiliations

  • Department of Mathematical Sciences, Clemson University, Clemson, USA

    Clark Jeffries

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