Combining Artificial Neural Nets : Ensemble and Modular Multi-Net Systems

Amanda J. C. Sharkey

ISBN 10: 185233004X / ISBN 13: 9781852330040

Published by Springer, Springer, 1999

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9781852330040

Bibliographic Details

• Title: Combining Artificial Neural Nets : Ensemble and Modular Multi-Net Systems
• Publisher: Springer, Springer
• Publication Date: 1999
• Language: English
• ISBN 10: 185233004X
• ISBN 13: 9781852330040
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 18D millimeters 235 millimeters width by 155 millimeters height by 18 millimeters depth
• Weight: 482 grams

About this title

Synopsis

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.