Skip to main content
SpringerLink
Log in

A New Parameter Identification Method for Type-1 TS Fuzzy Neural Network

  • Conference paper
  • First Online:
Book cover Advances in Neural Networks – ISNN 2018 (ISNN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10878))

Included in the following conference series:

Abstract

Conjugate gradient methods can be used with advantages such as fast convergence and low memory requirement in real applications. A conjugate gradient-based neuro-fuzzy learning algorithm for zero-order Takagi-Sugeno inference systems is proposed in this paper. Compared with the existing gradient-based algorithm, this method enhances the learning performance.

This work was supported in part by the National Natural Science Foundation of China (No. 61305075), the Natural Science Foundation of Shandong Province (No. ZR2015AL014, ZR201709220208) and the Fundamental Research Funds for the Central Universities (No. 15CX08011A, 18CX02036A).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wu, W., Li, L., Yang, J., Liu, Y.: A modified gradient-based neuro-fuzzy learning algorithm and its convergence. Inf. Sci. 180, 1630–1642 (2010)

    Article  Google Scholar 

  2. Wang, J., Wu, W., Zurada, J.M.: Deterministic convergence of conjugate gradient method for feedforward neural networks. Neurocomputing 74, 2368–2376 (2011)

    Article  Google Scholar 

  3. Hestenes, M.R., Stiefel, E.L.: Method of Conjugate Gradients for Solving Linear Systems. National Bureau of Standards, Washington (1952)

    MATH  Google Scholar 

  4. Fletcher, R., Reeves, C.M.: Function minimization by conjugate gradients. Comput. J. 7, 149–154 (1964)

    Article  MathSciNet  Google Scholar 

  5. Polak, E., Ribiere, G.: Note sur la convergence de directions conjugates. Revue Fran. d’Info. et de Rech. Oper. 16, 94–112 (1969)

    Google Scholar 

  6. Polyak, B.T.: The conjugate gradient method in extremal problems. USSR Comput. Math. Math. Phys. 9, 94–112 (1969)

    Article  Google Scholar 

  7. Ghosh, A., Pal, N.R., Das, J.: A fuzzy rule based approach to cloud cover estimation. Remote Sens. Environ. 100, 531–549 (2006)

    Article  Google Scholar 

  8. Chen, Y.C., Pal, N.R., Chung, I.F.: An integrated mechanism for feature selection and fuzzy rule extraction for classification. IEEE Trans. Fuzzy Syst. 20, 683–698 (2012)

    Article  Google Scholar 

  9. Bezdek, J.C., Keller, J., Krishnapuram, P., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer, Norwell (1999)

    Book  Google Scholar 

  10. Pal, N.R., Eluri, V.K., Mandal, G.K.: Fuzzy logic approaches to structure preserving dimensionality reduction. IEEE Trans. Fuzzy Syst. 10, 277–286 (2002)

    Article  Google Scholar 

  11. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. SMC-15, 116–132 (1985)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Information and Control Engineering, China University of Petroleum, Qingdao, 266580, China

    Tao Gao

  2. College of Mathematics and Statistics, Hengyang Normal University, Hengyang, 421000, China

    Long Li

  3. College of Science, China University of Petroleum, Qingdao, 266580, China

    Zhen Zhang & Jian Wang

  4. Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center, Jinan, 250014, China

    Zhanquan Sun

Authors
  1. Tao Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Long Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhanquan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tao Gao .

Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Doha, Qatar

    Tingwen Huang

  2. Sichuan University, Chengdu, China

    Jiancheng Lv

  3. Southeast University, Nanjing, China

    Changyin Sun

  4. United Institute of Informatics Problems, Minsk, Belarus

    Alexander V. Tuzikov

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gao, T., Li, L., Zhang, Z., Sun, Z., Wang, J. (2018). A New Parameter Identification Method for Type-1 TS Fuzzy Neural Network. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-92537-0_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92536-3

  • Online ISBN: 978-3-319-92537-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation