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Experimental Study on Learning of Neural Network Using Particle Swarm Optimization in Predictive Fuzzy for Pneumatic Servo System

  • Shenglin MuEmail author
  • Satoru Shibata
  • Tomonori Yamamoto
  • Seigo Goto
  • Shota Nakashima
  • Kanya Tanaka
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

Based on the scheme of predictive fuzzy control combined with neural network (NN) for pneumatic servo system, the learning of NN using Particle Swarm Optimization (PSO) is studied according to experimental investigation in this research. A group of positioning experiments using existent pneumatic servo system were designed to confirm the effectiveness and efficiency of the NN’s learning employing PSO in the imaginary plant construction for the pneumatic system in predictive fuzzy control. The analysis in the study was implemented comparing the results of traditional back-propagation (BP) type NN and the PSO type NN.

Keywords

Pneumatic servo system Position control Predictive fuzzy control Neural network Particle swarm optimization 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shenglin Mu
    • 1
    Email author
  • Satoru Shibata
    • 1
  • Tomonori Yamamoto
    • 1
  • Seigo Goto
    • 1
  • Shota Nakashima
    • 2
  • Kanya Tanaka
    • 3
  1. 1.Graduate School of Science and EngineeringEhime UniversityMatsuyamaJapan
  2. 2.Graduate School of Sciences and Technology for InnovationYamaguchi UniversityUbeJapan
  3. 3.School of Science and TechnologyMeiji UniversityKawasakiJapan

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