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Using Fuzzy Grey Cognitive Maps for Industrial Processes Control

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Fuzzy Cognitive Maps for Applied Sciences and Engineering

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 54))

Abstract

Recently, Fuzzy Grey Cognitive Maps (FGCM) has been proposed as a Grey System theory-based FCM extension. Grey systems have become a very effective theory for solving problems within environments with high uncertainty, under discrete small and incomplete data sets. The benefits of FGCMs over conventional FCMs make evident the significance of developing a greyness-based cognitive model such as FGCM. In this chapter, the FGCM model and the proposed NHL learning algorithm were applied within an industrial problem, concerning a chemical process control process with two tanks, three valves, one heating element and two thermometers for each tank. The proposed mathematical formulation of FGCMs and the implementation of the NHL algorithm have been successfully applied. This type of learning rule accompanied with the good knowledge of the given system, guarantee the successful implementation of the proposed technique in industrial process control problems.

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References

  1. Boutalis, Y., Kottas, T., Christodoulou, M.: Adaptive estimation of fuzzy cognitive maps with proven stability and parameter convergence. IEEE Trans. Fuzzy Syst. 17(4), 874–889 (2009)

    Article  Google Scholar 

  2. Bueno, S., Salmeron, J.L.: Benchmarking main activation functions in fuzzy cognitive maps. Expert Syst. Appl. 36, 5221–5229 (2009)

    Article  Google Scholar 

  3. Deng, J.L.: Introduction to grey system theory. J. Grey Syst. 1, 1–24 (1989)

    MATH  Google Scholar 

  4. Froelich, W., Papageorgiou, E.I., Samarinas, M., Skriapas, K.: Application of evolutionary FCMs to the long-term prediction of prostate cancer. Appl. Soft Comput. http://dx.doi.org/10.1016/j.asoc.2012.02.005

  5. Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24, 65–75 (1986)

    Article  MATH  Google Scholar 

  6. Kosko, B.: Fuzzy Engineering. Prentice-Hall, New Jersey (1996)

    Google Scholar 

  7. Li, G., Yamaguchia, D., Nagaib, M.: A grey-based decision-making approach to the supplier selection problem. Math. Comput. Modell. 46, 573–581 (2007)

    Article  Google Scholar 

  8. Liu, S., Lin, Y.: Grey Information. Springer, London (2006)

    Google Scholar 

  9. Mago, V.K., Mehta, R., Woolrych, R., Papageorgiou, E.I.: Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping. BMC Med. Inform. Decis. Mak. 12(98), 1–12 (2012)

    Google Scholar 

  10. Papageorgiou, E.I., Iakovidis, D.: Intuitionistic fuzzy cognitive maps. IEEE Trans. Fuzzy Syst. doi:10.1109/TFUZZ.2012.2214224

  11. Papageorgiou, E.I., Salmeron, J.L.: A Review of fuzzy cognitive map research at the last decade. IEEE Trans. Fuzzy Syst. (IEEE TFS), in press, http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06208855

  12. Papageorgiou, E.I., Groumpos, P.P.: A weight adaptation method for fine-tuning fuzzy cognitive map causal links. Soft Comput. J. 9, 846–857 (2005)

    Article  MATH  Google Scholar 

  13. Papageorgiou, E.I., Stylos, C., Groumpos, P.P.: Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links. Int. J. Hum Comput Stud. 64, 727–743 (2006)

    Article  Google Scholar 

  14. Papageorgiou, E.I., Salmeron, J.L.: Learning fuzzy grey cognitive maps using nonlinear hebbian-based approach. Int. J. Approximate Reasoning 53(1), 54–65 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  15. Salmeron, J.L.: Modelling grey uncertainty with fuzzy grey cognitive maps. Expert Syst. Appl. 37(12), 7581–7588 (2010)

    Article  Google Scholar 

  16. Salmeron, J.L.: Fuzzy cognitive maps for artificial emotions forecasting. Appl. Soft Comput. 12(12), 3704–3710 (2012)

    Article  Google Scholar 

  17. Salmeron, J.L., Gutierrez, E.: Fuzzy grey cognitive maps in reliability engineering. Appl. Soft Comput. 12(12), 3818–3824 (2012)

    Article  Google Scholar 

  18. Salmeron, J.L., Lopez, C.: Forecasting risk impact on ERP maintenance with augmented fuzzy cognitive maps. IEEE Trans. Software Eng. 38(2), 439–452 (2012)

    Article  Google Scholar 

  19. Salmeron, J.L., Vidal, R., Mena, A.: Ranking fuzzy cognitive maps based scenarios with TOPSIS. Expert Syst. Appl. 39(3), 2443–2450 (2012)

    Article  Google Scholar 

  20. Stylios, C., Georgopoulos, V., Groumpos, P.P.: Fuzzy cognitive map approach to process control systems. J. Adv. Comput. Intell. 3(5), 409–417 (1999)

    Google Scholar 

  21. Stylos, C., Goumpos, P.P.: Fuzzy cognitive maps in modeling supervisory control systems. J. Intell. Fuzzy Syst. 8(2), 83–98 (2000)

    Google Scholar 

  22. Wu, S.X., Li, M.Q., Cail, L.P., Liu, S.F.: A comparative study of some uncertain information theories. In: Proceedings of the International Conference on Control and Automation, 1114–1119 (2005)

    Google Scholar 

  23. Yamaguchi, D., Li, G., Chen, L., Nagai, M.: Reviewing crisp, fuzzy, grey and rough mathematical models. In: Proceedings of the IEEE International Conference on Grey Systems and Intelligent Services, 547–552 (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computational Intelligence Lab, University Pablo de Olavide, 1st km. Utrera Road, Seville, Spain

    Jose L. Salmeron

  2. Department of Computer Engineering, Technological Educational Institute of Central Greece, 3rd Km Old National Road Lamia-Athens, 35100, Lamia, Greece

    Elpiniki I. Papageorgiou

Authors
  1. Jose L. Salmeron
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  2. Elpiniki I. Papageorgiou
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Corresponding author

Correspondence to Jose L. Salmeron .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Technological Educational Institute of Central Greece, Lamia, Greece

    Elpiniki I. Papageorgiou

1 Electronic supplementary material

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Appendix

Appendix

$$\begin{aligned} A_1= \left[ \begin{array}{cccccccc} &{}[0.52,0.53] [0.56,0.56] [0.48,1.00] [0.63,1.00] [0.65,0.66] [0.57,0.58] [0.52,0.53] [0.64,0.70] \\ &{}[0.53,0.54] [0.57,0.57] [0.67,0.71] [0.79,1.00] [0.72,1.00] [0.58,0.59] [0.53,0.54] [0.66,0.71] \\ &{}[0.24,1.00] [0.55,0.61] [0.64,0.77] [0.64,0.77] [0.65,0.73] [0.56,0.63] [0.51,0.57] [0.63,0.77] \\ &{}[0.51,0.57] [0.50,1.00] [0.64,0.77] [0.64,0.78] [0.66,0.73] [0.56,0.63] [0.01,1.00] [0.63,0.78] \\ &{}[0.53,0.54] [0.56,0.57] [0.66,0.73] [0.66,0.73] [0.67,0.69] [0.58,0.59] [0.53,0.54] [0.65,0.74] \\ &{}[0.54,0.54] [0.57,0.58] [0.60,1.00] [0.67,0.72] [0.67,0.68] [0.59,0.59] [0.54,0.54] [0.66,1.00] \\ &{}[0.52,0.53] [0.56,0.56] [0.65,0.69] [0.59,1.00] [0.65,0.66] [0.57,0.58] [0.52,0.53] [0.64,0.70] \\ &{}[0.51,0.58] [0.54,0.62] [0.63,0.77] [0.63,0.78] [0.65,0.73] [0.18,1.00] [0.51,0.58] [0.62,0.78] \end{array} \right] \end{aligned}$$
(16)
$$\begin{aligned} A_2= \left[ \begin{array}{cccccccc} &{}[0.55,0.57] [0.60,0.61] [0.49,1.00] [0.65,1.00] [0.70,0.72] [0.61,0.63] [0.56,0.57] [0.68,0.75] \\ &{}[0.56,0.58] [0.61,0.62] [0.70,0.76] [0.80,1.00] [0.72,1.00] [0.62,0.63] [0.57,0.58] [0.69,0.77] \\ &{}[0.20,1.00] [0.57,0.65] [0.65,0.82] [0.66,0.83] [0.68,0.78] [0.58,0.67] [0.53,0.62] [0.64,0.83] \\ &{}[0.53,0.62] [0.48,1.00] [0.65,0.83] [0.65,0.84] [0.68,0.78] [0.58,0.68] [0.00,1.00] [0.64,0.84] \\ &{}[0.55,0.58] [0.59,0.62] [0.68,0.78] [0.68,0.79] [0.70,0.75] [0.61,0.64] [0.55,0.58] [0.67,0.79] \\ &{}[0.57,0.58] [0.61,0.62] [0.60,1.00] [0.70,0.78] [0.71,0.73] [0.62,0.64] [0.57,0.58] [0.66,1.00] \\ &{}[0.55,0.57] [0.60,0.61] [0.69,0.74] [0.60,1.00] [0.69,0.71] [0.61,0.62] [0.56,0.57] [0.67,0.75] \\ &{}[0.52,0.62] [0.56,0.67] [0.64,0.83] [0.65,0.84] [0.67,0.79] [0.16,1.00] [0.52,0.63] [0.63,0.84] \\ \end{array} \right] \end{aligned}$$
(17)
$$\begin{aligned} A_3= \left[ \begin{array}{cccccccc} &{}[0.58,0.61] [0.62,0.65] [0.50,1.00] [0.66,1.00] [0.74,0.77] [0.64,0.67] [0.58,0.61] [0.72,0.81] \\ &{}[0.59,0.61] [0.64,0.65] [0.73,0.81] [0.82,1.00] [0.75,1.00] [0.65,0.68] [0.60,0.62] [0.72,0.82] \\ &{}[0.17,1.00] [0.59,0.69] [0.66,0.87] [0.67,0.89] [0.70,0.84] [0.60,0.72] [0.55,0.66] [0.66,0.89] \\ &{}[0.54,0.66] [0.44,1.00] [0.65,0.88] [0.65,0.90] [0.70,0.85] [0.59,0.73] [0.00,1.00] [0.65,0.91] \\ &{}[0.56,0.61] [0.60,0.64] [0.68,0.82] [0.69,0.83] [0.73,0.79] [0.61,0.67] [0.57,0.62] [0.69,0.84] \\ &{}[0.58,0.62] [0.63,0.66] [0.60,1.00] [0.72,0.84] [0.74,0.80] [0.64,0.69] [0.59,0.63] [0.68,1.00] \\ &{}[0.58,0.61] [0.62,0.65] [0.71,0.80] [0.61,1.00] [0.73,0.78] [0.63,0.67] [0.58,0.62] [0.71,0.81] \\ &{}[0.53,0.66] [0.56,0.69] [0.63,0.88] [0.64,0.90] [0.69,0.84] [0.12,1.00] [0.53,0.67] [0.64,0.90] \end{array} \right] \end{aligned}$$
(18)

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Salmeron, J.L., Papageorgiou, E.I. (2014). Using Fuzzy Grey Cognitive Maps for Industrial Processes Control. In: Papageorgiou, E. (eds) Fuzzy Cognitive Maps for Applied Sciences and Engineering. Intelligent Systems Reference Library, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39739-4_14

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  • DOI: https://doi.org/10.1007/978-3-642-39739-4_14

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