Identification of time-series signals using a dynamic neural network with GA-based training

Kunihiko Nakazono, Kouhei Ohnishi, Hiroshi Kinjo

Research output: Contribution to journalArticlepeer-review


We propose a dynamic neural network (DNN) that realizes a dynamic property and has a network structure with the properties of inertia, viscosity, and stiffness without time-delayed input elements, and a training algorithm based on a genetic algorithm (GA). In a previous study, we proposed a modified training algorithm for the DNN based on the error back-propagation method. However, in the previous method it was necessary to determine the values of the DNN property parameters by trial and error. In the newly proposed DNN, the GA is designed to train not only the connecting weights but also the property parameters of the DNN. Simulation results show that the DNN trained by the GA obtains good performance for time-series patterns generated from an unknown system, and provides a higher performance than the conventional neural network.

Original languageEnglish
Pages (from-to)102-105
Number of pages4
JournalArtificial Life and Robotics
Issue number2
Publication statusPublished - 2006 Nov


  • Dynamic neural network
  • Genetic algorithm
  • Identification
  • Property parameter

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • Artificial Intelligence


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