Skip to main content
padlock icon - secure page this page is secure

Design of neural network model for analysing hydrostatic circular recessed bearings with axial piston pump slipper

Buy Article:

$40.56 + tax (Refund Policy)

A neural network is employed to analyze axial piston pump of hydrostatic circular recessed bearing. Owing to complexity of the system, the neural network is used to predict the bearing parameters of the experimental system. The system mainly consists of cylinder block, piston, slipper, ball-joint and swash plate. The neural model of the system has three layers, which are input layer with one neuron, hidden layer with ten neurons and output layer with three neurons. It can be outlined from the results for both approaches neural network could be modeled bearing systems in real time applications.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: Mechanical Components; Neural Nets; Pumps; Surface Pressure

Document Type: Research Article

Publication date: May 1, 2004

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more