iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/11760191_110
Design of RBF Network Based on Fuzzy Clustering Method for Modeling of Respiratory System | SpringerLink
Skip to main content
Springer Nature Link
Log in

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

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

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

Included in the following conference series:

  • 2237 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

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)

    Google Scholar 

  2. Gustafson, D.E., Kessel, W.C.: Fuzzy Clustering with a Fuzzy Covariance Matrix. In: Proceedings of the IEEE CDC, San Diego, CA, USA, pp. 761–766 (1979)

    Google Scholar 

  3. Babuska, R., Alic, L., Lourens, M.S.: Estimation of Respiratory Parameters via Fuzzy Clustering. Artificial Intelligence in Medicine 21, 91–105 (2001)

    Article  Google Scholar 

  4. Sagara, S., Zhao, Z.: Numerical Integration Approach to On-Line Identification of Continuous-Time Systems. Automatica 26, 63–74 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  5. Ljung, L.: System Identification Theory for the User. Prentice-Hall, Englewood Cliffs (1987)

    MATH  Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shunshoku Kanae
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zi-Jiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kiyoshi Wada
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • 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)

Publish with us

Policies and ethics

Search

Navigation