Skip to main content

Multilayer Neural Network Model for Safe Evaluation of Amusement Ride

  • Conference paper
  • First Online:
  • 3427 Accesses

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 134))

Abstract

The three layers safety evaluation model of amusement ride was created according to fuzzy comprehensive evaluation. The five layers neural network model was created according to neural network model. Corresponding to three layers safety evaluation model, the neural network model includes input layer, output layer and three hidden layers. The safety evaluation and neural network system was developed and implemented with Visual C++6.0. Using multilayer neural network and training samples, the evaluation factor weights change with the evaluating process and time.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Yan, W.T.: Research on urban ecosystem health attribute synthetic assessment model and application. Systems Engineering and Theory & Practice 27(8), 137–145 (2007)

    Google Scholar 

  2. Zhang, W.H., Du, B.C., Yang, Y.Y.: Application of fuzzy analytic hierarchy process of TV and radio information assurance evaluation indicator systems. Acta Electronica Sinica 36(10), 2060–2064 (2008)

    Google Scholar 

  3. Jia, S.K., Wen, X.H., Lin, D.J., Jiang, Z.A.: Study on Safety Assessement Techniques for Subway Operation System Based on Hierarchy Process. China Safety Science Journal 5(5), 137–141 (2008)

    Google Scholar 

  4. Du, Y.X., Tian, Q.H.: Performance evaluation for mechanical products based on fuzzy neural network. System Engineering and Electronics 27(9), 1583–1586 (2005)

    Google Scholar 

  5. Zhao, W., Ye, S.: Fyzzy neural network based on q-Gaussian and its application in operational effectiveness evaluation of planes. Transactions of Beijing Institute of Technology 30(6), 674–677, 682 (2010)

    Google Scholar 

  6. Rumelhart, D.E., Hinton, G.H., Williams, R.J.: Learning representations by back-propagetiong errors. Nature 323, 533–536 (1986)

    Article  Google Scholar 

  7. Ren, X.X., Li, Q., Wu, Z.Y.: Computer aided decision-making system for staff promotion based on fuzzy neural network. Journal of Southwest Jiaotong University 41, 245–249 (2006)

    MATH  Google Scholar 

  8. Saleh, K.: Documenting Electronic Commerce Systems and Software Using the Unified Modeling Language. Information and Software Technology 5, 303–311 (2002)

    Article  Google Scholar 

  9. Jain, H., Vitharana, P., Zahedi, F.M.: An Assessment Model for Requirements Identification in Component-based Software Development. ACM SIGMIS Database 34, 48–63 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuan Xiao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xiao, Y., Guan, J., Ye, J. (2011). Multilayer Neural Network Model for Safe Evaluation of Amusement Ride. In: Zheng, D. (eds) Advances in Electrical Engineering and Electrical Machines. Lecture Notes in Electrical Engineering, vol 134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25905-0_96

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-25905-0_96

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25904-3

  • Online ISBN: 978-3-642-25905-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics