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Design of RBF Network Based on Fuzzy Clustering Method for Modeling of Respiratory System

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

Pulmonary elastance provides an important basis for deciding air pressure parameters of mechanical ventilators, and airway resistance is an important parameter in the diagnosis of respiratory diseases. The authors have proposed a second order nonlinear differential equation model of respiratory system whose elastic and resistant coefficients are expressed by RBF networks with the lung volume as the input. When we use RBF networks expression, numerical stability can be expected, because the output of each node is in range of [0,1], the balance between each node is good. However, the problems of deciding the number of nodes and the center/deviation of each node were remained. In this paper, a design method of RBF network based on fuzzy clustering method is proposed to decide center and deviation of each node. By means of fuzzy clustering, the available data set is partitioned into fuzzy subsets so that each RBF works effectively. The proposed method is validated by examples of application to practical clinical data.

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References

  1. Maeda, K., Kanae, S., Yang, Z.J., Wada, K.: Estimation of Respiratory Parameters Based on RBF Network. In: The 23rd SICE Kyushu Branch Annual Conference, December 4–5, pp. 133–136. Kitakyushu Science and Research Park (2004)

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Systems Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University, Email:jin@ees.kyushu-u.ac.jp, Hakozaki 6-10-1, Higashi-ku, Fukuoka, 812-8581, Japan

    Kouji Maeda, Shunshoku Kanae, Zi-Jiang Yang & Kiyoshi Wada

Authors
  1. Kouji Maeda
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  2. Shunshoku Kanae
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  3. Zi-Jiang Yang
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  4. Kiyoshi Wada
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Maeda, K., Kanae, S., Yang, ZJ., Wada, K. (2006). Design of RBF Network Based on Fuzzy Clustering Method for Modeling of Respiratory System. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_110

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  • DOI: https://doi.org/10.1007/11760191_110

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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