Fundamental theory of artificial higher order neural networks

Madan M. Gupta; Noriyasu Homma; Zeng Guang Hou; Ashu M.G. Solo; Takakuni Goto

doi:10.4018/978-1-59904-897-0.ch017

Fundamental theory of artificial higher order neural networks

Madan M. Gupta, Noriyasu Homma, Zeng Guang Hou, Ashu M.G. Solo, Takakuni Goto

MED - Health Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter

12 Citations (Scopus)

Abstract

In this chapter, we aim to describe fundamental principles of artificial higher order neural units (AHO-NUs) 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.

Original language	English
Title of host publication	Artificial Higher Order Neural Networks for Economics and Business
Publisher	IGI Global
Pages	368-388
Number of pages	21
ISBN (Print)	9781599048970
DOIs	https://doi.org/10.4018/978-1-59904-897-0.ch017
Publication status	Published - 2008

ASJC Scopus subject areas

Computer Science(all)

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10.4018/978-1-59904-897-0.ch017

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ER -

Fundamental theory of artificial higher order neural networks

Abstract

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