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

10 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)

Access to Document

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

Cite this

@inbook{5ca51b4fbba3487da678a7e35f81bfd2,

title = "Fundamental theory of artificial higher order neural networks",

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.",

author = "Gupta, {Madan M.} and Noriyasu Homma and Hou, {Zeng Guang} and Solo, {Ashu M.G.} and Takakuni Goto",

year = "2008",

doi = "10.4018/978-1-59904-897-0.ch017",

language = "English",

isbn = "9781599048970",

pages = "368--388",

booktitle = "Artificial Higher Order Neural Networks for Economics and Business",

publisher = "IGI Global",

}

TY - CHAP

T1 - Fundamental theory of artificial higher order neural networks

AU - Gupta, Madan M.

AU - Homma, Noriyasu

AU - Hou, Zeng Guang

AU - Solo, Ashu M.G.

AU - Goto, Takakuni

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=77956612106&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956612106&partnerID=8YFLogxK

U2 - 10.4018/978-1-59904-897-0.ch017

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

M3 - Chapter

AN - SCOPUS:77956612106

SN - 9781599048970

SP - 368

EP - 388

BT - Artificial Higher Order Neural Networks for Economics and Business

PB - IGI Global

ER -

Fundamental theory of artificial higher order neural networks

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this