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Multi-degree Prosthetic Hand Control Using a New BP Neural Network

  • R. C. Wang
  • F. Li
  • M. Wu
  • J. Z. Wang
  • L. Jiang
  • H. Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

A human-like multi-fingered prosthetic hand, HIT hand, has been developed in Harbin Institute of Technology. This paper presents a new pattern discrimination method for HIT hand control. The method uses a bagged-BP neural network based on combing the BP neural networks using bagging algorithm. Bagging has been used to overcome the problem of limited number of training data in uni-model systems, by combining neural networks as weak learners. We compared the results of the bagging based BP network, using four features, with the results obtained separately from these uni-feature systems. The results show that the bagged-BP network improves both the accuracy and stability of the BP classifier.

Keywords

Finger Movement Robot Hand Hybrid Feature Discrimination Rate Mean Absolute Value 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • R. C. Wang
    • 1
  • F. Li
    • 1
  • M. Wu
    • 2
  • J. Z. Wang
    • 1
  • L. Jiang
    • 3
  • H. Liu
    • 3
  1. 1.Division of Intelligent and Biomechanical System, State Key Laboratory of TribologyTsinghua UniversityBeijingChina
  2. 2.Northwestern UniversityUSA
  3. 3.Robotics Research Institute, Harbin Institute of TechnologyHarbinChina

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