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Neural Computing & Applications

, Volume 13, Issue 2, pp 99–100 | Cite as

Special issue on ‘Neural networks for enhanced intelligence’

  • Damminda Alahakoon
  • Ajith Abraham
  • Lakhmi Jain
Editorial
  • 54 Downloads

Preface

Artificial neural network (ANN) models comprise networks of interconnected adaptive processing units. A very important feature of these networks is their adaptive nature, which enables the replacement of traditional ‘programming’ with the ability of ‘learning by examples’. This feature makes these models very attractive in applications where there is very little, or incomplete, understanding of the problem, but where sufficient data is available for network training. ANNs have been found to be useful in a wide variety of problems, including pattern classification, speech recognition, function approximation, image and data compression, clustering, forecasting, and prediction.

‘Intelligence’ has many definitions and one of the popular definitions is—the ability to comprehend; to understand and profit from experience. ANNs have been categorized as intelligent systems, which provide new technology in enhancing the intelligence of traditional applications. This special issue...

Keywords

Artificial Neural Network Radial Basis Function Time Series Forecast Language Disorder Hopfield Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

  1. 1.School of Business SystemsMonash UniversityClaytonAustralia

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