Neural networks to solve nonlinear inverse kinematic problems

Fusaomi Nataga, Maki K. Habib, Keigo Watanabe

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In making neural networks learn nonlinear relations effectively, it is desired to have appropriate trainingsets. In the proposed method, after a certain number of iterations, input-output pairs having worseerrors are extracted from the original training set and form a new temporary set. From the followingiteration, the temporary set is applied to the neural networks instead of the original set. In this case,only pairs with worse errors are used for updating the weights until the mean value of errors decreasesto a desired level. Once the learning is conducted using the temporary set, the original set is appliedagain instead of the temporary set. The effectiveness of the proposed approach is demonstrated throughsimulations using kinematic models of a leg module with a serial link structure and an industrial robot.

Original languageEnglish
Title of host publicationHandbook of Research on Biomimetics and Biomedical Robotics
PublisherIGI Global
Pages205-227
Number of pages23
ISBN (Electronic)9781522529941
ISBN (Print)1522529934, 9781522529934
DOIs
Publication statusPublished - Dec 15 2017

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)
  • Chemical Engineering(all)
  • Biochemistry, Genetics and Molecular Biology(all)

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  • Cite this

    Nataga, F., Habib, M. K., & Watanabe, K. (2017). Neural networks to solve nonlinear inverse kinematic problems. In Handbook of Research on Biomimetics and Biomedical Robotics (pp. 205-227). IGI Global. https://doi.org/10.4018/978-1-5225-2993-4.ch009