Skip to main content
Springer Nature Link
Log in

The isolation layered optimization algorithm of MIMO polygonal fuzzy neural network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The single-input single-output or multi-input single-output polygonal fuzzy neural network can accomplish some information disposing based on a finite number of points of polygonal fuzzy number. Although it does not depend on a precise mathematical model, it involves logical reasoning, numerical calculation and nonlinear functional approximation. The multi-input multi-output (MIMO) polygonal fuzzy neural network model is proposed for the first time in this article. The two different algorithms are designed in the input layer and hidden layer, respectively, and some parameters of the connection weights in the isolation layered manner can be optimized. Particularly, the neurons in the hidden layer are optimized one by one. Results showed that the isolation layered optimization algorithm of MIMO polygonal fuzzy neural network could improve the computational efficiency and convergent rate.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Aliev RA, Guirimov BG, Fazlollahi B, Alive RR (2009) Evolutionary algorithm-based learning of fuzzy neural networks. Part 2: recurrent fuzzy neural networks. Fuzzy Sets Syst 160(17):2553–2566. doi:10.1016/j.fss.2008.12.018

    Article  MathSciNet  MATH  Google Scholar 

  2. Anitha J, Jude HD (2013) An efficient Kohonen-fuzzy neural network based abnormal retinal image classification system. Neural Netw World 23(2):149–167. doi:10.14311/NNW.2013.23.011

    Article  Google Scholar 

  3. Chung FL, Duan JC (2000) On multistage fuzzy neural network modeling. IEEE Trans Fuzzy Syst 8(2):125–142. doi:10.1109/91.842148

    Article  Google Scholar 

  4. Er MJ, Wu S, Gao Y (2001) A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans Fuzzy Syst 9(4):578–594. doi:10.1109/91.940970

    Article  Google Scholar 

  5. Chen TP, Chen H (1993) Approximation of continuous functionals by neural networks with applications to dynamic systems. IEEE Trans Neural Netw 6(4):910–918. doi:10.1109/72.286886

    Article  Google Scholar 

  6. Feuring T, Lippe WM (1999) The fuzzy neural network approximation lemma. Fuzzy Sets Syst 102(2):227–237. doi:10.1016/S0165-0114(97)00125-5

    Article  MathSciNet  MATH  Google Scholar 

  7. Liu PY (2001) Universal approximation of continuous analyses fuzzy valued functions by multi-layer regular fuzzy neural networks. Fuzzy Sets Syst 119(3):313–320. doi:10.1016/S0165-0114(99)00132-3

    Article  MATH  Google Scholar 

  8. Rumelhart DE, Hinton CE, Wiliams RJ (1986) Learning representations by back-propagating errors. Nature 323(9):533–536. doi:10.1038/323533a0

    Article  MATH  Google Scholar 

  9. Tian CJ, Wei G (2001) A new layer-wise linearized algorithm for feedforward neural network. Acta Electron Sin 29(11):1495–1498

    Google Scholar 

  10. Xie H, Cheng HZ, Niu DX, Zhang GL (2005) A fast learning algorithm for feedfoward neural network based on the layer-by-layer and neuron-by-neuron optimizing procedure. Acta Electron Sin 33(1):111–114

    Google Scholar 

  11. Tao YQ, Cui DW (2011) Load identification of algorithm based on dynamic fuzzy granular neural network. Control Decis 26(4):519–523+529

    MathSciNet  Google Scholar 

  12. Wu JL, Zhang BH, Wang K (2012) Application of adaboost-based BP neural network for short-term wind speed forecast. Power Styst Technol 36(9):221–225

    Google Scholar 

  13. Zhou Y, Wang S, Gao CS, Sun WC (2010) An improved PSO-based fuzzy neural network and its application in short-term weather forecast. Comput Appl Softw 27(5):234–237

    Google Scholar 

  14. Liu PY (2002) A new fuzzy neural network and its approximation capability. Sci China Ser E 32(1):76–86

    Google Scholar 

  15. He CM, Ye YP (2011) Evolution computation based learning algorithms of polygonal fuzzy neural networks. Int J Intell Syst 262(4):340–352. doi:10.1002/int.20469

    Article  MATH  Google Scholar 

  16. Wang GJ, Li XP (2011) Universal approximation of polygonal fuzzy neural networks in sense of \(K\)-integral norms. Sci China Inf Sci 54(11):2307–2323. doi:10.1007/s11432-011-4364-y

    Article  MathSciNet  MATH  Google Scholar 

  17. Wang GJ, Li XP (2014) Construction of the polygonal fuzzy neural network and its approximation based on K-integral norm. Neural Netw World 24(4):357–376. doi:10.14311/NNW.2014.24.021

    Article  Google Scholar 

  18. He Y, Wang GJ (2012) Conjugate gradient algorithm of the polygonal fuzzy neural networks. Acta Electron Sin 40(10):2079–2084. doi:10.3969/j.issn.0372-2112.2012.10.029

    Google Scholar 

  19. Sui XL, Wang GJ (2012) Influence of perturbations of training pattern pairs on stability of polygonal fuzzy neural network. Pattern Recognit Artif Intell 26(6):928–936

    Google Scholar 

  20. Yang YQ, Wang GJ, Yang Y (2014) Parameter optimization of polygonal fuzzy neural networks based on GA-BP hybrid algorithm. Int J Mach Learn Cybernet 5(5):815–822. doi:10.1007/s13042-013-0224-y

    Article  Google Scholar 

  21. Wang GJ, He Y, Li XP (2015) Optimization algorithms for MISO polygonal fuzzy neural networks. Sci China Inf Sci 45(5):650–667. doi:10.1360/N112013-00214

    Google Scholar 

Download references

Acknowledgment

This work has been supported by National Natural Science Foundation China (Grant No. 61374009).

Author information

Authors and Affiliations

  1. School of Mathematics Science, Tianjin Normal University, Tianjin, 300387, China

    Guijun Wang

  2. School of Mathematics, Tonghua Normal University, Tonghua, 134002, China

    Chunfeng Suo

Authors
  1. Guijun Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Chunfeng Suo
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Guijun Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, G., Suo, C. The isolation layered optimization algorithm of MIMO polygonal fuzzy neural network. Neural Comput & Applic 29, 721–731 (2018). https://doi.org/10.1007/s00521-016-2600-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2600-5

Keywords