Skip to main content

An Intelligent System of Diagnosis Based on Associative Factor Uncertainty Speculation Inference

  • Conference paper

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 104))

Abstract

Along with the development of computer technology and Artificial Intelligence, it has been a highlight of Intelligent Diagnosis System how to solve diagnosis problem more accurately and smoothly by the help of AI system.In this paper, some novel concepts such as Key Factor, Associative Factor and Uncertainty Speculation are initiated. Furthermore, we pioneer a method so-called Uncertainty Inference based on Key-Associative Uncertainty Speculation, into which an innovative speculation mechanism is proposed and integrated. Study of cases and experiment statistics makes it clear that our scenario is practical, effective, and with a satisfying accurate rate of diagnosis.

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   259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Hu, J.-d., Yu, Y.-c.: Technology of a fertilizing expert system PDA for crop growing. Transactions of The Chinese Society of Agricultural Engineering 22, 149 (2006)

    Google Scholar 

  2. Tan, w.-x., Wang, x.-p., Xi, j.-j., et al.: Research on diagnosing disease method based on back propagation neural network. Computer Engineering Design 32, 1070 (2011)

    Google Scholar 

  3. Liao, Y., Liang, J., Liao, C., et al.: Research on the application of multi-combined technologies of fault diagnosis on missile seekers. Journal of Astronautics 3, 30 (2006)

    Google Scholar 

  4. Liu, S.-w., Wang, Q.-w.: Grape disease diagnosis system based on fuzzy neural network. Transactions of the Chinese Society of Agricultural Engineering 18, 144 (2006)

    Google Scholar 

  5. Tianhong, L.: Integration of large scale fertilizing models with GIS using minimum unit. Environmental Modeling and Software 18, 221 (2003)

    Article  Google Scholar 

  6. Xi, J.-j., Li, S.: Research on sheep disease diagnostic expert system based on subjective Bayesian algorithm with self-learning. Journal of Hunan University of Science and Technology 24, 95 (2009)

    Google Scholar 

  7. Yang, P., Zhuang, C.-l.: Design and implementation of Web-based teleconsultation system for fish disease diagnosis. Transactions of The Chinese Society of Agricultural Engineering 22, 127 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tan, W., Wang, X., Xu, X. (2011). An Intelligent System of Diagnosis Based on Associative Factor Uncertainty Speculation Inference. In: Jin, D., Lin, S. (eds) Advances in Computer Science, Intelligent System and Environment. Advances in Intelligent and Soft Computing, vol 104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23777-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23777-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23776-8

  • Online ISBN: 978-3-642-23777-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics