SpringerLink
Log in

Intelligent Systems in Industry

A Realistic Overview

  • Chapter
  • First Online:
Innovative Issues in Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 623))

Abstract

The objective of this paper is to give a realistic overview of the current state of the art of intelligent systems in industry based on the experience from applying these systems in a large global corporation. It includes a short analysis of the differences between academic and industrial research, examples of the key implementation areas of intelligent systems in manufacturing and business, a discussion about the main factors for success and failure of industrial intelligent systems, and an estimate of the projected industrial needs that may drive future applications of intelligent systems.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 85.59
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 106.99
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 106.99
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Angelov, P., Kordon, A.: Evolving inferential sensors in the chemical industry. In: Angelov, P., Filev, D., Kasabov, N. (eds.) Evolving Intelligent Systems: Methodology and Applications, pp. 313–336. Wiley, New York (2010)

    Google Scholar 

  2. Brabazon, A., O’Neil, A., Dempsey, I.: An introduction to evolutionary computation in finance. IEEE Comput. Intell. Mag. 3, 42–55 (2008)

    Article  Google Scholar 

  3. Becker, Y., Fei, P., Lester, A.: Stock selection: an innovative application of genetic programming methodology. In: Riolo, R., Soule, T., Worzel, B. (eds.) Genetic Programming Theory and Practice IV, pp. 315–335. Springer, Berlin (2007)

    Google Scholar 

  4. Bonissone, P., Chen, Y., Goebel, K., Khedkar, P.: Hybrid soft computing systems: industrial and commercial applications. Proc. IEEE 87(9), 1641–1667 (1999)

    Article  Google Scholar 

  5. Cawse, J. (ed.): Experimental Design for Combinatorial and High-Throughput Materials Development. Wiley, New York (2003)

    Google Scholar 

  6. Clements, M., Franses, P., Swanson, N.: Forecasting economic and financial time-series with non-linear models. Int. J. Forecast. 20, 169–183 (2004)

    Article  Google Scholar 

  7. Conradie, A., Aldrich, C.: Development of neurocontrollers with evolutionary reinforcement learning. Comput. Chem. Eng. 30(1), 1–17 (2006)

    Article  Google Scholar 

  8. Davenport, T., Harris, J., Morrison, R.: Analytics at Work: Smarter Decisions Better Results. Harvard Business Press (2010)

    Google Scholar 

  9. Fortuna, L., Graziani, S., Rizzo, A., **bilia, M.: Soft Sensors for Monitoring and Control of Industrial Processes. Springer, Berlin (2007)

    Google Scholar 

  10. Gusikhin, O., Rychtyckyj, N., Filev, D.: Intelligent systems in the automotive industry: applications and trends. Knowl. Inf. Syst. 12(2), 147–168 (2007)

    Article  Google Scholar 

  11. Jordaan, E., Kordon, A., Smits, G., Chiang, L.: Robust inferential sensors based on ensemble of predictors generated by genetic programming. In: Proceedings of PPSN 2004, pp. 522–531. Springer, Berlin (2004)

    Google Scholar 

  12. Kadlec, P., Gabrys, B., Strandt, S.: Data-driven soft sensors in the process industry. Comput. Chem. Eng. 33, 795–814 (2009)

    Article  Google Scholar 

  13. Kalos, A., Kordon, A., Smits, G., Werkmeister, S.: Hybrid model development methodology for industrial soft sensors. In: Proceedings of the IEEE ACC 2003, Denver, CO, pp. 5417–5422 (2003)

    Google Scholar 

  14. Kordon, A.: Hybrid intelligent systems for industrial data analysis. Int. J. Intell. Syst. 19, 367–383 (2004)

    Article  Google Scholar 

  15. Kordon, A.: Applying Computational Intelligence: How to Create Value. Springer, Berlin (2010)

    Google Scholar 

  16. Kordon, A., Smits, G.: Soft sensor development using genetic programming. In: Proceedings of GECCO 2001, San Francisco, pp. 1346–1351 (2001)

    Google Scholar 

  17. Kordon, A., Smits, G., Jordaan, E., Rightor, E.: Robust soft sensors based on integration of genetic programming, analytical neural networks, and support vector machines. In: Proceedings of WCCI 2002, Honolulu, pp. 896–901 (2002)

    Google Scholar 

  18. Kordon, A., Jordaan, E., Chew, L., Smits, G., Bruck, T., Haney, K., Jenings, A.: Biomass inferential sensor based on ensemble of models generated by genetic programming. In: Proceedings of GECCO 2004, Seattle, WA, pp. 1078–1089 (2004)

    Google Scholar 

  19. Kordon, A., Jordaan, E., Castillo, F., Kalos, A., Smits, G., Kotanchek, M.: Competitive advantages of evolutionary computation for industrial applications. In: Proceedings of CEC 2005, Edinburgh, UK, pp. 166–173 (2005)

    Google Scholar 

  20. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  21. Li, Y., Ang, K., Chong, G.: Patents, software, and hardware for PID control. IEEE Control Syst. Mag. 26(1), 42–54 (2006)

    Article  Google Scholar 

  22. Luck, M., McBurney, P., Shehory, O., Willmott, S.: Agent Technology Roadmap. AgentLink III (2005)

    Google Scholar 

  23. Minelli, M., Chambers, M., Dhiraj, A.: Big Data, Big Analytics. Wiley, New York (2013)

    Google Scholar 

  24. Rey, T., Kordon, A., Wells, C.: Applied Data Mining for Forecasting Using SAS. SAS Press (2012)

    Google Scholar 

  25. Schwartz, D.: Concurrent marketing analysis: a multi-agent model for product, price, place, and promotion. Mark. Intell. Plann. 18(1), 24–29 (2000)

    Article  Google Scholar 

  26. Seavy, K., Jones, A., Kordon, A.: Hybrid genetic programming—first-principles approach to process and product modeling. Ind. Eng. Chem. Res. 49(5), 2273–2285 (2010)

    Article  Google Scholar 

  27. Siegel, A., Etzkorn, I.: Simple: Conquering the Crisis of Complexity. Twelve, New York (2013)

    Google Scholar 

  28. Smits, G., Kotachenek, M.: Pareto-front exploitation symbolic regression. In: O’Reiley, U.M., Yu, T., Riolo, R., Worzel, B. (eds.) Genetic Programming Theory and Practice II, pp. 283–300. Springer, New York (2004)

    Google Scholar 

  29. Smits, G., Kordon, A., Jordaan, E., Vladislavleva, C., Kotanchek, M.: Variable selection in industrial data sets using pareto genetic programming. In: Yu, T., Riolo, R., Worzel, B. (eds.) Genetic Programming Theory and Practice III, pp. 79–92. Springer, New York (2006)

    Chapter  Google Scholar 

  30. Stefanov, Z., Chiang, L., Kordon, A.: Successful industrial application of robust inferential sensors for NOx emissions monitoring. In: Proceedings of AIChE (2008)

    Google Scholar 

  31. Zadeh, L.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 90, 103–111 (1996)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Advanced Analytics Group, The Dow Chemical Company, 2301 N Brazosport Blvd, Freeport, TX, 77566, US

    Arthur Kordon

Authors
  1. Arthur Kordon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arthur Kordon .

Editor information

Editors and Affiliations

  1. Inst of Info & Commu Tech, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Vassil Sgurev

  2. Iona College, Machine Intelligence Institute, New Rochelle, New York, USA

    Ronald R. Yager

  3. Sys Res Intit of Polish Acad of Science, Intelligent Systems Laboratory, Warsaw, Poland

    Janusz Kacprzyk

  4. and Information Technologies, University of Library Studies, Sofia, Bulgaria

    Vladimir Jotsov

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Kordon, A. (2016). Intelligent Systems in Industry. In: Sgurev, V., Yager, R., Kacprzyk, J., Jotsov, V. (eds) Innovative Issues in Intelligent Systems. Studies in Computational Intelligence, vol 623. Springer, Cham. https://doi.org/10.1007/978-3-319-27267-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27267-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27266-5

  • Online ISBN: 978-3-319-27267-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation