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Fundamental Theory of Artificial Higher Order Neural Networks

Fundamental Theory of Artificial Higher Order Neural Networks
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Author(s): Madan M. Gupta (University of Saskatchewan, Canada), Noriyasu Homma (Tohoku University, Japan), Zeng-Guang Hou (The Chinese Academy of Sciences, China), Ashu M. G. Solo (Maverick Technologies America Inc., USA) and Takakuni Goto (Tohoku University, Japan)
Copyright: 2009
Pages: 20
Source title: Artificial Higher Order Neural Networks for Economics and Business
Source Author(s)/Editor(s): Ming Zhang (Christopher Newport University, USA)
DOI: 10.4018/978-1-59904-897-0.ch017

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Abstract

In this chapter, we aim to describe fundamental principles of artificial higher order neural units (AHONUs) and networks (AHONNs). An essential core of AHONNs can be found in higher order weighted combinations or correlations between the input variables. By using some typical examples, this chapter describes how and why higher order combinations or correlations can be effective.

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