On-line prediction of escaping fish from catching net by neural network and circular approximation

Toshiaki Yoshida, Hidekazu Suzuki, Mamoru Minami, Yasushi Mae

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


This paper presents a method to predict a fish motion by Neural Network (N.N.) with on-line learning. We assume the motion trajectory of a fish swimming in a pool be approximated by circles with time varying radius and center position. We try to improve prediction accuracy of a fish motion by using N.N. whose inputs are radii and angular velocities in previous three control-times and outputs are radius and angular velocity in the following control period. Using radius and angular velocity obtained by circular approximation, we confirmed that the proposed N.N. prediction system can maintain good prediction performances under the proposed on-line learning process.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Number of pages6
Publication statusPublished - 2006
Externally publishedYes
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: Oct 18 2006Oct 21 2006

Publication series

Name2006 SICE-ICASE International Joint Conference


Other2006 SICE-ICASE International Joint Conference
Country/TerritoryKorea, Republic of


  • Back propagation
  • Gazing-GA
  • Neural network
  • Prediction

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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