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

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Cognitive Internet of Things: Frameworks, Tools and Applications (ISAIR 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 810))

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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.

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References

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Authors and Affiliations

  1. Graduate School of Science and Engineering, Ehime University, 3 Bunkyo-cho, Matsuyama, Ehime, 7908577, Japan

    Shenglin Mu, Satoru Shibata, Tomonori Yamamoto & Seigo Goto

  2. Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 2-16-1 Tokiwa-dai, Ube, Yamaguchi, 7558611, Japan

    Shota Nakashima

  3. School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 2148571, Japan

    Kanya Tanaka

Authors
  1. Shenglin Mu
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  2. Satoru Shibata
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  3. Tomonori Yamamoto
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  4. Seigo Goto
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  5. Shota Nakashima
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  6. Kanya Tanaka
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Corresponding author

Correspondence to Shenglin Mu .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

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Mu, S., Shibata, S., Yamamoto, T., Goto, S., Nakashima, S., Tanaka, K. (2020). Experimental Study on Learning of Neural Network Using Particle Swarm Optimization in Predictive Fuzzy for Pneumatic Servo System. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_32

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