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Artificial Neural Networks

  • Antonio Mucherino
  • Petraq J. Papajorgji
  • Panos M. Pardalos
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 34)

Abstract

In the early days of artificial intelligence (AI), artificial neural networks (ANNs) were considered a promising approach to find good learning algorithms to solve practical application problems [189]. Perhaps, a certain unjustified hype was associated to their use, since, nowadays, ANNs seem to have less appeal for researchers. In fact, they are not considered to be among the top 10 data mining techniques [237]. Moreover, publications using ANNs are found not to be backed by a sound statistical analysis [75] and that statistical evaluation of ANNs experiments is a necessity [74]. There are, however, applications in which ANNs have been successfully used. Among such applications, there are the applications in the agricultural-related areas which are discussed in Section 5.4 of this chapter. Therefore, even though they may not be so appealing for some researchers anymore, we decided to dedicate this chapter to ANNs.

Keywords

Neural Network Hide Layer Output Layer Input Layer Hide Neuron 
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.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Antonio Mucherino
    • 1
  • Petraq J. Papajorgji
    • 1
  • Panos M. Pardalos
    • 2
  1. 1.Institute of Food & Agricultural Information Technology OfficeUniversity of FloridaGainesvilleUSA
  2. 2.Department of Industrial & Systems EngineeringUniversity of FloridaGainesvilleUSA

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