Skip to main content
SpringerLink
Log in

A Type-2 Fuzzy Hybrid Expert System for Commercial Burglary

  • Conference paper
  • First Online:
Fuzzy Logic in Intelligent System Design (NAFIPS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 648))

Included in the following conference series:

Abstract

In this paper, an interval type-2 fuzzy hybrid expert system is proposed for commercial burglary. This method is the combination of Sugeno and Mamdani inference system. After identifying the system domain, the inputs and output of the system are determined. Then the k-nearest neighborhood functional dependency approach is used to select the most important variables for the system. The indirect approach is used to fuzzy system modeling by implementing the Kwon validity index for determining the number of rules in the fuzzy clustering approach. Next, the output membership values are projected onto the input spaces to generate the membership values of input variables, and the membership functions of inputs and output are tuned. Then, the type-1 fuzzy hybrid system has been implemented. After that, we transformed the type-1 fuzzy hybrid rule base into an interval type-2 fuzzy hybrid rule base for enhancing the robustness of the system. For generating interval type-2 fuzzy hybrid rule base, the Gaussian primary MF with an uncertain standard deviation and a fixed mean is used. In order to validate our method, we developed two system modeling techniques and compared the results with the proposed interval type-2 fuzzy hybrid expert system. These techniques are multiple regression, and type-1 fuzzy expert system. The results of this study show that the proposed interval type-2 fuzzy hybrid expert system has a better performance in comparison to type-1 fuzzy and multiple regression models.

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

Access this chapter

Log in via an institution
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 EPUB and 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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Van Dijk, J.J.: Towards effective public-private partnerships in crime control: experiences in the Netherlands. In: Business and Crime Prevention, pp. 99–124 (1997)

    Google Scholar 

  2. Nee, C., Taylor, M.: Examining burglars’ target selection: interview, experiment or ethnomethodology? Psychol. Crime Law 6(1), 45–59 (2000)

    Article  Google Scholar 

  3. Gill, M. (ed.): Crime at Work. Studies in Security and Crime Prevention, vol. 1. Springer, Heidelberg (2016)

    Google Scholar 

  4. Maguire, M., Bennett, T.: Burglary in a Dwelling: The Offence, the Offender, and the Victim. Heinemann, London (1982)

    Google Scholar 

  5. Kandel, A.: Fuzzy Expert Systems. CRC Press, Boca Raton (1991)

    MATH  Google Scholar 

  6. Fazel Zarandi, M.H., Gamasaee, R.: Type-2 fuzzy hybrid expert system for prediction of tardiness in scheduling of steel continuous casting process. Soft Comput. 16(8), 128–302 (2012)

    Article  Google Scholar 

  7. Sotudian, S., Fazel Zarandi, M.H., Turksen, I.B.: From Type-I to Type-II fuzzy system modeling for diagnosis of hepatitis. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 10(7), 1238–1246 (2016)

    Google Scholar 

  8. Etik, N., Allahverdi, N., Sert, I.U., Saritas, I.: Fuzzy expert system design for operating room air-condition control systems. Expert Syst. Appl. 36(6), 9753–9758 (2009)

    Article  Google Scholar 

  9. Fazel Zarandi, M.H., Gamasaee, R., Turksen, I.B.: A type-2 fuzzy expert system based on a hybrid inference method for steel industry. Int. J. Adv. Manuf. Technol. 71(5–8), 857–885 (2014)

    Article  Google Scholar 

  10. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 8(3), 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  11. Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)

    Article  Google Scholar 

  12. Wu, D., Tan, W.W.: A type-2 fuzzy logic controller for the liquid-level process. In: IEEE International Conference on Fuzzy Systems, Proceedings, vol. 2, pp. 953–958, 25 July 2004

    Google Scholar 

  13. Fazel Zarandi, M.F., Rezaee, B., Turksen, I.B., Neshat, E.: A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Syst. Appl. 36(1), 139–154 (2009)

    Article  Google Scholar 

  14. Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)

    Article  Google Scholar 

  15. Uncu, Ö., Türkşen, I.B.: A novel feature selection approach: combining feature wrappers and filters. Inf. Sci. 177(2), 449–466 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  16. Kwon, S.H.: Cluster validity index for fuzzy clustering. Electr. Lett. 34(22), 2176–2178 (1998)

    Article  Google Scholar 

  17. Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1(1), 7–31 (1993)

    Article  Google Scholar 

  18. Fazel Zarandi, M.H.: Aggregate system analysis for prediction of tardiness and mixed zones of continuous casting with fuzzy methodology. Ph.D. thesis (1998)

    Google Scholar 

  19. Mendel, J.M.: Uncertainty, fuzzy logic, and signal processing. Sig. Process. 80(6), 913–933 (2000)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

    M. H. Fazel Zarandi, A. Seifi, H. Esmaeeli & Sh. Sotudian

Authors
  1. M. H. Fazel Zarandi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Seifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Esmaeeli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sh. Sotudian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. H. Fazel Zarandi .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Reformat

  5. Laboratory of Computational Intelligence and Automation, University of Waterloo, Waterloo, Ontario, Canada

    William Melek

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Fazel Zarandi, M.H., Seifi, A., Esmaeeli, H., Sotudian, S. (2018). A Type-2 Fuzzy Hybrid Expert System for Commercial Burglary. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67137-6_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67136-9

  • Online ISBN: 978-3-319-67137-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation