Predicting axial piston pump performance using neural networks

Mansour Karkoub, Osama E. Gad, Mahmoud G. Rabie

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

A neural network model for an axial piston pump (bent-axis design) is derived in this paper. The model uses data obtained from an experimental setup. The purpose of this ongoing study is the reduction of the power loss at high pressures. However, at the beginning, a study is being done to predict the behavior of the current design of the pump. The neural network model has a feedforward architecture and uses the Levenberg-Marquardt optimization technique in the training process. The model was able to predict the behavior of the pump accurately.

Original languageEnglish
Pages (from-to)1211-1226
Number of pages16
JournalMechanism and Machine Theory
Volume34
Issue number8
DOIs
Publication statusPublished - Nov 1999
Externally publishedYes

Fingerprint

Reciprocating pumps
Neural networks
Pumps

ASJC Scopus subject areas

  • Bioengineering
  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications

Cite this

Predicting axial piston pump performance using neural networks. / Karkoub, Mansour; Gad, Osama E.; Rabie, Mahmoud G.

In: Mechanism and Machine Theory, Vol. 34, No. 8, 11.1999, p. 1211-1226.

Research output: Contribution to journalArticle

Karkoub, Mansour ; Gad, Osama E. ; Rabie, Mahmoud G. / Predicting axial piston pump performance using neural networks. In: Mechanism and Machine Theory. 1999 ; Vol. 34, No. 8. pp. 1211-1226.
@article{2c11902555f042b8bed141a4ceec701a,
title = "Predicting axial piston pump performance using neural networks",
abstract = "A neural network model for an axial piston pump (bent-axis design) is derived in this paper. The model uses data obtained from an experimental setup. The purpose of this ongoing study is the reduction of the power loss at high pressures. However, at the beginning, a study is being done to predict the behavior of the current design of the pump. The neural network model has a feedforward architecture and uses the Levenberg-Marquardt optimization technique in the training process. The model was able to predict the behavior of the pump accurately.",
author = "Mansour Karkoub and Gad, {Osama E.} and Rabie, {Mahmoud G.}",
year = "1999",
month = "11",
doi = "10.1016/S0094-114X(98)00086-X",
language = "English",
volume = "34",
pages = "1211--1226",
journal = "Mechanism and Machine Theory",
issn = "0374-1052",
publisher = "Elsevier Limited",
number = "8",

}

TY - JOUR

T1 - Predicting axial piston pump performance using neural networks

AU - Karkoub, Mansour

AU - Gad, Osama E.

AU - Rabie, Mahmoud G.

PY - 1999/11

Y1 - 1999/11

N2 - A neural network model for an axial piston pump (bent-axis design) is derived in this paper. The model uses data obtained from an experimental setup. The purpose of this ongoing study is the reduction of the power loss at high pressures. However, at the beginning, a study is being done to predict the behavior of the current design of the pump. The neural network model has a feedforward architecture and uses the Levenberg-Marquardt optimization technique in the training process. The model was able to predict the behavior of the pump accurately.

AB - A neural network model for an axial piston pump (bent-axis design) is derived in this paper. The model uses data obtained from an experimental setup. The purpose of this ongoing study is the reduction of the power loss at high pressures. However, at the beginning, a study is being done to predict the behavior of the current design of the pump. The neural network model has a feedforward architecture and uses the Levenberg-Marquardt optimization technique in the training process. The model was able to predict the behavior of the pump accurately.

UR - http://www.scopus.com/inward/record.url?scp=0032630584&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032630584&partnerID=8YFLogxK

U2 - 10.1016/S0094-114X(98)00086-X

DO - 10.1016/S0094-114X(98)00086-X

M3 - Article

VL - 34

SP - 1211

EP - 1226

JO - Mechanism and Machine Theory

JF - Mechanism and Machine Theory

SN - 0374-1052

IS - 8

ER -