Skip to main content
SpringerLink
Log in

Novel method of flatness pattern recognition via cloud neural network

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Aiming at the weakness of the existing cloud neural network on training and practicality, a new improved structure of cloud neural network is designed. A hidden layer is added prior to the inverse cloud layer. Threshold level is set to zero and a simple training method is designed. In addition, considering the ignorance of signal randomness and fuzziness in the existing method of the flatness signal recognition, the cloud neural network combines the advantages of the fuzziness and randomness of cloud model and the learning and memory ability of neural network. Thus it is applied in the flatness signal recognition. The simulation contrast results demonstrate that the improved structure is able to identify common defects in shape with higher identity precision.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  • Bordignon F, Gomide F (2014) Uninorm based evolving neural networks and approximation capabilities. Neurocomputing 127:13–20

    Article  Google Scholar 

  • Jia CY, Shan XY, Liu HM (2008) Fuzzy neural model for flatness pattern recognition. J Iron Steel Res 15(6):33–38

    Article  Google Scholar 

  • Jung JY, Im YT (1997) Simulation of fuzzy shape control for cold-rolled strip with randomly irregular strip shape. Mater Process Technol 63(1–3):248–254

    Article  Google Scholar 

  • Li DY, Liu CY, Liu LY (2004) Study on the universality of the normal cloud model. Eng Sci 6(8):28–34

    Google Scholar 

  • Liu CY, Feng M, Dai XJ (2004) A new algorithm of inverse cloud. J System Simul 16(11):2417–2420

  • Liu J, Wang YQ, Sun F (2008) Fuzzy pattern recognition method of flatness based on particle swarm theory. Chin J Mech Eng 44(1):173–178

  • Lu XW, Peng Y, Liu HM (2011) Gauge and shape control strategy for DC mill. J Central South Univ 42(8):2309–2317

    Google Scholar 

  • Peng Y, Liu HM, Wang DC (2007) Simulation of type selection for 6-high cold tandem mill based on shape control ability. J Central South Univ Technol 14(2):278–284

    Article  MathSciNet  Google Scholar 

  • Ren HP, Liu D, Zheng G (2002) A genetic algorithm approach to shape pattern recognition. Heavy Machin 3:9–12

    Google Scholar 

  • Rubio JJ (2014a) Analytic neural network model of a wind turbine. Soft Computing. doi:10.1007/s00500-014-1290-0

  • Rubio JJ (2014b) Evolving intelligent algorithms for the modelling of brain and eye signals. Appl Soft Comput 14:259–268

    Article  Google Scholar 

  • Shan XY, Liu HM, Jia CY (2010) A recognition method of new flatness pattern containing the cubic flatness. Iron Steel Res 45(8):56–60

    Google Scholar 

  • Silva AM, Caminhas W, Lemos A, Gomide F (2014) A fast learning algorithm for evolving neo-fuzzy neuron. Appl Soft Comput 14(8):194–209

    Article  Google Scholar 

  • Su YH (2007) Cold-rolled strip production and prospects of China. Metall Inf Rev 5:44–48

    Google Scholar 

  • Tian YQ (2003) Research on theory and application of data mining based on cloud model. Shanghai Jiaotong University, Shanghai

    Google Scholar 

  • Xu LJ (2007) Flatness control in cold strip rolling and mill type selection. Metallurgical Industry Press, Beijing

    Google Scholar 

  • Xu ZB, Fan ZZ (2009) Cloud neural network based fault detection and diagnosis of space propulsion system. Acta Armamentarii 30(6):727–732

    Google Scholar 

  • Zhang XL, Liu HM (2003) GA-BP model of flatness pattern recognition and improved least-squares method. Iron Steel Res 38(10):29–34

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Xiu-ling Zhang

  2. National Engineering Research Center for Equipment and Technology of Cold StripRolling, Qinhuangdao, 066004, China

    Xiu-ling Zhang

  3. Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Liang Zhao, Wen-bao Zhao & Teng Xu

Authors
  1. Xiu-ling Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-bao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Teng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiu-ling Zhang.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Xl., Zhao, L., Zhao, Wb. et al. Novel method of flatness pattern recognition via cloud neural network. Soft Comput 19, 2837–2843 (2015). https://doi.org/10.1007/s00500-014-1445-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-014-1445-z

Keywords

Search

Navigation