Description

• Title:
  APPLYING NEURAL NETWORK IN COMPUTING FILLING COEFFICIENT OF FOUR-STROKE INTERNAL COMBUSTION ENGINE
• Creator:
  Bera, Piotr
• Description:
  Neural networks consist of many simple elements operating in parallel. In supervised training they are capable of finding their own solution to a particular problem, given only examples of proper behavior. It is a very useful method of solving complex, non-linear problems. The following article discusses the usage of artificial neural network to compute the value of filling coefficient of four-stroke internal combustion engines as the function of crankshaft rotational speed and throttle opening angle. The paper presents the idea of a static, two-layer feedforward network trained with the basic backpropagation algorithm in which the weights and biases are updated in the direction of the negative gradient. The article discusses network architecture and data structure, training parameters and result analysis.
• Object availability:
  free access
• Rights:
  Copyright (c) 2011 Mechanics and Control
• Date:
  2011.06.26
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion;
  Peer-reviewed Article
• Language:
  English
• Source:
  Mechanics and Control; Vol. 30 No. 2 (2011);
  2300-7079;
  2083-6759
• Format:
  application/pdf
• Publisher:
  AGH University of Science and Technology Press
• Relation: https://journals.agh.edu.pl/mech/article/view/1604/1108
• Subject:
  neural network;
  supervised training;
  backpropagation;
  internal combustion engine;
  filling coefficient
• Identifier: https://journals.agh.edu.pl/mech/article/view/1604
• Data provider:
  AGH University of Science and Technology Journals
• Can I use it?:
  seek permission
• Type:
  text