© 1999

Combining Artificial Neural Nets

Ensemble and Modular Multi-Net Systems

  • Amanda J. C. Sharkey

Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Amanda J. C. Sharkey
    Pages 1-30
  3. Amanda J. C. Sharkey
    Pages 31-50
  4. Amanda J. C. Sharkey
    Pages 51-78
  5. Amanda J. C. Sharkey
    Pages 163-178
  6. Amanda J. C. Sharkey
    Pages 179-204
  7. Amanda J. C. Sharkey
    Pages 267-295
  8. Back Matter
    Pages 297-298

About this book


The past decade could be seen as the heyday of neurocomputing: in which the capabilities of monolithic nets have been well explored and exploited. The question then is where do we go from here? A logical next step is to examine the potential offered by combinations of artificial neural nets, and it is that step that the chapters in this volume represent. Intuitively, it makes sense to look at combining ANNs. Clearly complex biological systems and brains rely on modularity. Similarly the principles of modularity, and of reliability through redundancy, can be found in many disparate areas, from the idea of decision by jury, through to hardware re­ dundancy in aeroplanes, and the advantages of modular design and reuse advocated by object-oriented programmers. And it is not surprising to find that the same principles can be usefully applied in the field of neurocomput­ ing as well, although finding the best way of adapting them is a subject of on-going research.


Ensembl cognition genetic algorithm neural network proving simulation speech recognition

Editors and affiliations

  • Amanda J. C. Sharkey
    • 1
  1. 1.Department of Computer ScienceUniversity of SheffieldSheffieldUK

Bibliographic information