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Extending Neuro-fuzzy Techniques with Grey-Based Hybridisation

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Emerging Studies and Applications of Grey Systems

Part of the book series: Series on Grey System ((SGS))

Abstract

Real-world problems are commonly composed by interrelated components in many complex ways. They are usually dynamic, that is, they change with time through a series of interactions among related components. Classical decision-making techniques cannot support these kinds of challenges. For that reason, this paper focused on the extension of neuro-fuzzy techniques as Fuzzy Cognitive Maps with Grey Systems Theory.

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References

  • Bueno, S., & Salmeron, J. L. (2009). Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications, 36(3 Part 1), 5221–5229.

    Google Scholar 

  • Deng, J. (1989). Introduction to grey system theory. The Journal of Grey System, 1(1), 1–24.

    Google Scholar 

  • Fu, L. (1991). Causim: A rule-based causal simulation system. Simulation, 56(4).

    Google Scholar 

  • Furfaro, R., Kargel, J. S., Lunine, J. I., Fink, W., & Bishop, M. P. (2010). Identification of cryovolcanism on Titan using fuzzy cognitive maps. Planetary and Space Science, 58(5), 761–779.

    Article  Google Scholar 

  • Kang, I., Sangjae, L., & Choi, J. (2004). Using fuzzy cognitive map for the relationship management in airline service. Expert Systems with Applications, 26, 545–555.

    Article  Google Scholar 

  • Kosko, B. (1986). Fuzzy cognitive maps. International Journal on Man-Machine Studies, 24, 65–75.

    Article  Google Scholar 

  • Kosko, B. (1996). Fuzzy engineering. Prentice-Hall.

    Google Scholar 

  • Lee, K. C., Kim, J. S., Chung, H. N., & Kwon, S. J. (2002). Fuzzy cognitive map approach to web-mining inference amplification. Expert Systems with Applications, 22, 197–211.

    Google Scholar 

  • Liu, S., & Lin, Y. (2006). Grey information. Springer.

    Google Scholar 

  • Lopez, C., & Salmeron, J. L. (2013). Dynamic risks modelling in erp maintenance projects with fcm. Information Sciences, 256, 25–45.

    Article  Google Scholar 

  • Nápoles, G., Salmeron, J. L., & Vanhoof, K. (2021). Construction and supervised learning of long-term grey cognitive networks. IEEE Transactions on Cybernetics, 51(2), 686–695.

    Article  Google Scholar 

  • Papageorgiou, E. (2011). A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Applied Soft Computing, 11(1), 500–513.

    Article  Google Scholar 

  • Papageorgiou, E., & Groumpos, P. (2005). A weight adaptation method for fine-tuning fuzzy cognitive map causal links. Soft Computing Journal, 9, 846–857.

    Article  Google Scholar 

  • Papageorgiou, E., & Salmeron, J. L. (2013). A review of fuzzy cognitive map research at the last decade. IEEE Transactions on Fuzzy Systems, 21(1), 66–79.

    Article  Google Scholar 

  • Pelaez, C., & Bowles, J. (1995). Applying fuzzy cognitive maps knowledge representation to failure models effects analysis. In IEEE Annual Reliability and Maintainability Symposium.

    Google Scholar 

  • Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. L. (2007). Modelling it projects success with fuzzy cognitive maps. Expert Systems with Applications, 32, 543–559.

    Article  Google Scholar 

  • Salmeron, J. L. (2009a). Augmented fuzzy cognitive maps for modelling LMS critical success factors. Knowledge-Based Systems, 22(4), 275–278.

    Article  Google Scholar 

  • Salmeron, J. L. (2009b). Supporting decision makers with fuzzy cognitive maps. Research-Technology Management, 52(3), 7581–7588.

    Article  Google Scholar 

  • Salmeron, J. L. (2010). Modelling grey uncertainty with fuzzy grey cognitive maps. Expert Systems with Applications, 37(12), 7581–7588.

    Article  Google Scholar 

  • Salmeron, J. L. (2012). Fuzzy cognitive maps for artificial emotions forecasting. Applied Soft Computing, 12(12), 3704–3710.

    Article  Google Scholar 

  • Salmeron, J. L., & Froelich, W. (2016). Dynamic optimization of fuzzy cognitive maps for time series forecasting. Knowledge-Based Systems, 105, 29–37.

    Article  Google Scholar 

  • Salmeron, J. L., & Gutierrez, E. (2012). Fuzzy grey cognitive maps in reliability engineering. Applied Soft Computing, 12(12), 3818–3824.

    Google Scholar 

  • Salmeron, J. L., & Lopez, C. (2012). Forecasting risk impact on erp maintenance with augmented fuzzy cognitive maps. IEEE Transactions on Software Engineering, 38(2), 439–452.

    Article  Google Scholar 

  • Salmeron, J. L., & Palos-Sanchez, P. R. (2019). Uncertainty propagation in Fuzzy Grey Cognitive Maps with Hebbian-like learning algorithms. IEEE Transactions on Cybernetics, 49(1), 211–220.

    Article  Google Scholar 

  • Salmeron, J. L., & Papageorgiou, E. L. (2012). A fuzzy grey cognitive maps-based decision support system for radiotherapy treatment planning. Knowledge-Based Systems, 30(1), 151–160.

    Article  Google Scholar 

  • Salmeron, J. L., & Papageorgiou, E. (2014). Fuzzy grey cognitive maps and nonlinear Hebbian learning in process control. Applied Intelligence, 41(1), 223–234.

    Article  Google Scholar 

  • Stylios, C. D., & Groumpos, P. P. (2000). Fuzzy cognitive maps in modeling supervisory control systems. Journal of Intelligent & Fuzzy Systems, 8(2), 83–98.

    Google Scholar 

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

    Google Scholar 

  • Yang, Y., & John, R. (2012). Grey sets and greyness. Information Sciences, 185(1), 249–264.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. CUNEF Universidad, Madrid, Spain

    Jose L. Salmeron

Authors
  1. Jose L. Salmeron
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Correspondence to Jose L. Salmeron .

Editor information

Editors and Affiliations

  1. Faculty of Technology, School of Computer Science, De Montfort University, Leicester, Leicestershire, UK

    Yingjie Yang

  2. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Sifeng Liu

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Salmeron, J.L. (2023). Extending Neuro-fuzzy Techniques with Grey-Based Hybridisation. In: Yang, Y., Liu, S. (eds) Emerging Studies and Applications of Grey Systems. Series on Grey System. Springer, Singapore. https://doi.org/10.1007/978-981-19-3424-7_4

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