Skip to main content

Research and Application of A Integrated System

—Textile Smart ProcessUsing ANN combined with CBR

  • Conference paper
Advances in Systems, Computing Sciences and Software Engineering

Abstract

In this pqper, the disadvantages and advantages of artificial neural networks (ANNs) and Case-base Reasoning (CBR) have been briefly introduced respectively. The capacity of network can be Improved through the mechanisum of CBR in the dynamic processing environment. And the limitation of CBR, that could not complete their reasoning process and propose a solution to a given task without intervention of experts, can be strong self-learning ability of ANN. The combination of these two artificial intelligent techniques not only benefits to control the quality and enhance the efficiency, but also to shorten the design cycle and save the cost, which paly an important role in promoting the intelligentized level of the textile industry. At the same time, utilizing ANN prediciting model, the sensitive process variables that affect the processing performances and quality of yarn and fabric can be decided, which are often adjusted during solving the new problems to form the desired techniques.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. J. Keeler. Vision of neural networks and fuzzy logic for prediction and optimization of manufacturing processes. In “Applications of Artificial Neural Networks” III (SPIE Vol. 1709), 1992, 447-456

    Google Scholar 

  2. M.C. Ramesh, R. Rajamanickam, and S. Jayaraman. The presiction of Yarn Tentile Properties by Using Artificial Neural Networks. J. Text Inst., 1995,86, 459~469

    Google Scholar 

  3. A. Guha, R. Chattopadhyay, and Jayadeva. Predicting yarn tenacity: A comparison of mechanistic, statical, and neural networks models. Journal of the Textile Institute, 2001, 92(2), 139~145

    Article  Google Scholar 

  4. F.H. She, L. X. Kong, S. Nahavandi, and A.Z. Kouzani. Intelligent Animal Fiber Classification with Artificial Neeural NEtworks. Textile Res. J., 2002, 72(7), 594~600

    Google Scholar 

  5. C. Luo and C. Hossein. Color Grading of Cotton PArt II: Color Grading with an Expert System and Neural Networks. Textile Res. J., 1999, 69(12), 893~903

    Google Scholar 

  6. C. Luo and D. L. Adams. Yarn Strength Prediction Using Neural Networks Part I : Fiber Properties and Yarn Strength Relationship. Textile Res. J., 1995, 65(9), 495~500

    Google Scholar 

  7. F. Pynckels, P. Kierkens, S. Sette, L. Van Langenhove, and K. Impe. USe of Neurl Nets for Determining the Spinnability of Fibers. J. Textile Inst., 1995, 86(3), 425~437

    Google Scholar 

  8. S. Sette, L. Bopullart, L. Van Langenhove, and P. Kiekens. Optimizing the Fiber-to-Yarn Production Process with a Combined Neural Network/Genetic Algorithm Approach. Textile Res. J., 1997, 67(2), 84~92

    Google Scholar 

  9. Huang Chang chiun and Yu Wenhong. Fuzzy Neural NEtwork Approach to Classifying Dyeing Defects. Textile Res. J., 2001, 71(1), 100~104

    Google Scholar 

  10. Chen Peiwen, Liang Tsair-chun, Yau Hon-fai, Sun Wan-li, Wang Nai-chuch, Lin Horng-chyi, and Lien Rong-cherng. Classifying Textile Faults with a Back-propagation Neural Network Using Power Spectra. Textile Res. J., 1998, 68(2), 121~126

    Google Scholar 

  11. J. Fan and L. Hunter. A worsted Fabric Expert System Part II: An Artificial Neural Network Model for Predicting the Properties of Worstes Fabrics. Textile Res. J., 1998, 68(10), 763~771

    Google Scholar 

  12. C.L. Hui, T.W. Lau, and S.F. Ng. Neural Network Prediction of Human Psychological Perceptions of Fabric Hand. Textile Res. J., 2004, 74(5), 375~383

    Google Scholar 

  13. A.S.W. Wong, Y. Li, and P.K.W. Yeung. Predicting Clothing Sensory Comfort with Artificial Intelligence Hybrid Models. Textkile Res. J., 2004, 74(1), 13~19

    Article  Google Scholar 

  14. A. Vellido, P.J.G. Lisboa, and J. Vaughan. Neural networks in business: a survey of applications (1992-1998). Expert Systems with Applications, 1995, 17, 51–70

    Article  Google Scholar 

  15. R. Rajamanickam, S. M. Hansen, and S. Jayaraman. Analysis of the Modeling Methodologies for Presicting the Strength of Air-Jet Spun Yarns. Textile Research Journal, 1997, 67(1), 39~44

    Google Scholar 

  16. M. Colilla, C. J. Fernandez, and E. Ruiz-Hitzky. Case-based reasoning (CBR) for multicomponent amnalysis using sensor arrays: Application to water quality evaluation. Analyst, 2002, 127:1580~1582

    Article  Google Scholar 

  17. B. Lees and C. Irgens. Knowledge based support for quality in engineering design. Procs. Eleventh International Conference on Expert Systems and Their Application, avignon, May 1991, 257~266

    Google Scholar 

  18. B. Lees, N. Rees, and J. Aiken. Knowledge-based oceabographic data analysis. Procs. Expersys-92, October 1992, 561~565

    Google Scholar 

  19. R. Sun and L. Bookman. How do symbols and networks fit together? AI MAgazine, 1993, 14(2):20~23

    Google Scholar 

  20. B. Less and J. Corchado. Integrated case-based neural network approach to problem solving. Proceedings of the 5th Biannual German Conference on Knowledge-Based Systems: Knowledge-Based /Systems- Survey and Future Directions. March 1999, 157~166

    Google Scholar 

  21. J.M. Corchado, B. Lees, and N. Rees. A multi-agent system “testbed” for evaluating automous agents. Proceedings of the first international conference on Autonomous agents, California, Unites States, 1997, 386~393

    Google Scholar 

  22. S. Krovvidy and W. C. Wee. Wastewater treatrment system from case-based reasoning. Machine learning, 1993, 10:341~363

    Article  Google Scholar 

  23. G. Kock. The neural network description language CONNECT, and its C == implementation. Technical report, GMD FIRST Berlin, Universitat Politechnia de Catalunya, August 1996

    Google Scholar 

  24. J. Corchado and B. Lees. A hybrid case-based model for forcasting. Applied Artificial Intelligence, 2001, 15(2/1): 105~127

    Google Scholar 

  25. Daqing Chen and P. Burrell. Case-based reasoning system and artificial neural networks: Areview. Neural Computing & Application, 2001, 10: 264~276

    Article  MATH  Google Scholar 

  26. J. Kolodener. Case-Based Reasoning, San MAteo, CA: Morgan KAufmann, 1993

    Google Scholar 

  27. A. Aamodt and E. Plaza. Case-based reasoning: foundational issues, methodological variations, and system apporaches. AI Communication, 1994, 7(1):39~59

    Google Scholar 

  28. I. Waston. Appllying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann, 1997

    Google Scholar 

  29. D. E. Rumelhart, G. E. Hinton, and R. mJ. Williams. Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructures of Cognition. D. E. Rumelhart an J. L. McClelland, Eds., MIT Press, Cambridge, MA, 1986, vol. 1, 318~362

    Google Scholar 

  30. E. Rich and K. Knight /artificial Intellifence. McGraw-Hill, New York, NY, USA, 1991, 487~524

    Google Scholar 

  31. J. Hertz, Z. Krogh, and R. G. Palmer. Introduction to the theory of neural computation. Addison-Wesley, Reading, MAA, USA, 1991

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer

About this paper

Cite this paper

YIN, X., YU*, W. (2006). Research and Application of A Integrated System. In: Sobh, T., Elleithy, K. (eds) Advances in Systems, Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/1-4020-5263-4_50

Download citation

  • DOI: https://doi.org/10.1007/1-4020-5263-4_50

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-5262-0

  • Online ISBN: 978-1-4020-5263-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics