Skip to main content
SpringerLink
Log in

Fuzzy System Optimization Using a Hierarchical Genetic Algorithm Applied to Pattern Recognition

  • Conference paper
Intelligent Systems'2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 323))

Abstract

In this paper a new method of hierarchical genetic algorithm for fuzzy inference systems optimization is proposed. This method was used to perform the combination of responses of modular neural networks for human recognition based on face, iris, ear and voice. The main idea of this paper is to perform the optimization of some parameters of fuzzy inference system, such as: type of fuzzy logic, type of system, number of fuzzy membership function in each variable, percentage of rules, type of membership functions (Trapezoidal or Gaussian) and parameters The results obtained using the hierarchical genetic algorithm show to have better results than non-optimized fuzzy inference as can be verified with the results.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Azamm, F.: Biologically Inspired Modular Neural Networks. PhD thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia (May 2000)

    Google Scholar 

  2. Castillo, O., Melin, P., Alanis Garza, A., Montiel, O., Sepúlveda, R.: Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms. Soft Comput. 15(6), 1145–1160 (2011)

    Article  Google Scholar 

  3. Castillo, O., Melin, P., Pedrycz, W.: Design of interval type-2 fuzzy models through optimal granularity allocation. Appl. Soft Comput. 11(8), 5590–5601 (2011)

    Article  Google Scholar 

  4. Castillo, O., Melin, P.: 3 Type-2 Fuzzy Logic. In: Castillo, O., Melin, P. Type-2 Fuzzy Logic Theory and Applications. STUDFUZZ, vol. 223, pp. 29–43. Springer, Heidelberg (2008)

    Google Scholar 

  5. Castillo, O., Melin, P.: Soft Computing for Control of Non-Linear Dynamical Systems. STUDFUZZ, vol. 63. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  6. Castro, J.R., Castillo, O., Melin, P., Rodríguez Díaz, A.: A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks. Inf. Sci. 179(13), 2175–2193 (2009)

    Article  MATH  Google Scholar 

  7. Castro, J.R., Castillo, O., Melin, P.: An Interval Type-2 Fuzzy Logic Toolbox for Control Applications. In: FUZZ-IEEE 2007, pp. 1–6 (2007)

    Google Scholar 

  8. Castro, J.R., Castillo, O., Melin, P., Rodriguez-Diaz, A.: Building Fuzzy Inference Systems with a New Interval Type-2 Fuzzy Logic Toolbox. Transactions on Computational Science 1, 104–114 (2008)

    Google Scholar 

  9. Coley, A.: An Introduction to Genetic Algorithms for Scientists and Engineers, Har/Dskt edn. Wspc (1999)

    Google Scholar 

  10. Database Ear Recognition Laboratory from the University of Science & Technology Beijing (USTB). Found on the Web page, http://www.ustb.edu.cn/resb/en/index.htmasp (accessed September 21, 2009)

  11. Database of Face. Institute of Automation of Chinese Academy of Sciences (CASIA). Found on the Web page, http://biometrics.idealtest.org/dbDetailForUser.do?id=9 (accessed November 11, 2012)

  12. Database of Human Iris. Institute of Automation of Chinese Academy of Sciences (CASIA). Found on the Web page, http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp (accessed September 21, 2009)

  13. Fausett, L.: Fundamentals of Neural Networks Architectures Algorithms and Applications. Prentice Hall (1994)

    Google Scholar 

  14. Gaxiola, F., Melin, P., Valdez, F., Castillo, O.: Optimization of type-2 fuzzy weight for neural network using genetic algorithm and particle swarm optimization. In: NaBIC 2013, pp. 22–28 (2013)

    Google Scholar 

  15. Haupt, R., Haupt, S.: Practical Genetic Algorithms, vol. 2, pp. 42–43. Wiley-Interscience (2004)

    Google Scholar 

  16. Hidalgo, D., Castillo, O., Melin, P.: Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms. Inf. Sci. 179(13), 2123–2145 (2009)

    Article  Google Scholar 

  17. Hidalgo, D., Melin, P., Castillo, O.: An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms. Expert Syst. Appl. 39(4), 4590–4598 (2012)

    Article  Google Scholar 

  18. Huang, J., Wechsler, H.: Eye Location Using Genetic Algorithm. In: Second International Conferenceon Audio and Video-Based Biometric Person Authentication, pp. 130–135 (1999)

    Google Scholar 

  19. Martínez-Soto, R., Castillo, O., Aguilar, L.T.: Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms. Inf. Sci. 179(13), 2158–2174 (2009)

    Article  Google Scholar 

  20. Melin, P., Castillo, O.: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems, 1st edn. STUDFUZZ, vol. 172, pp. 119–122. Springer, Heidelberg (2005)

    Google Scholar 

  21. Mitchell, M.: An Introduction to Genetic Algorithms, 3rd edn. A Bradford Book (1998)

    Google Scholar 

  22. Nawa, N., Takeshi, F., Hashiyama, T., Uchikawa, Y.: A study on the discovery of relevant fuzzy rules using pseudobacterial genetic algorithm. IEEE Transactions on Industrial Electronics 46(6), 1080–1089 (1999)

    Article  Google Scholar 

  23. Raikova, R.T., Aladjov, H.T.: Hierarchical genetic algorithm versus static optimization investigation of elbow flexion and extension movements. Journal of Biomechanics 35, 1123–1135 (2002)

    Article  Google Scholar 

  24. Sánchez, D., Melin, P.: Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure. Engineering Applications of Artificial Intelligence 27, 41–56 (2014)

    Article  Google Scholar 

  25. Sepúlveda, R., Melin, P., Rodríguez Díaz, A., Mancilla, A., Montiel, O.: Analyzing the effects of the Footprint of Uncertainty in Type-2 Fuzzy Logic Controllers. Engineering Letters 13(2), 138–147 (2006)

    Google Scholar 

  26. Tang, K.S., Man, K.F., Kwong, S., Liu, Z.F.: Minimal Fuzzy Memberships and Rule Using Hierarchical Genetic Algorithms. IEEE Trans. Ind. Electron. 45(1), 162–169 (1998)

    Article  Google Scholar 

  27. Wang, C., Soh, Y.C., Wang, H., Wang, H.: A Hierarchical Genetic Algorithm for Path Planning in a Static Environment with Obstacles. In: Canadian Conference on Electrical and Computer Engineering, IEEE CCECE 2002, vol. 3, pp. 1652–1657 (2002)

    Google Scholar 

  28. Worapradya, K., Pratishthananda, S.: Fuzzy supervisory PI controller using hierarchical genetic algorithms. In: 5th Asian Control Conference 2004, vol. 3, pp. 1523–1528 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Daniela Sánchez, Patricia Melin & Oscar Castillo

Authors
  1. Daniela Sánchez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patricia Melin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oscar Castillo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniela Sánchez .

Editor information

Editors and Affiliations

  1. Ford Motor Company, Research & Advanced Engineering, Dearborn, Mississippi, USA

    D. Filev

  2. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    J. Jabłkowski

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

    J. Kacprzyk

  4. Polish Academy of Sciences and WIT - Warsaw School of Information Technology, Systems Research Institute, Warsaw, Poland

    M. Krawczak

  5. Bulgarian Academy of Sciences, Institute of Information and Communication, Sofia, Bulgaria

    I. Popchev

  6. Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland

    L. Rutkowski

  7. Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Sofia, Bulgaria

    V. Sgurev

  8. Department of Computer and Information Technologies, “Prof. Assen Zlatarov" University Faculty of Technical Sciences, Bourgas, Bulgaria

    E. Sotirova

  9. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    P. Szynkarczyk

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

    S. Zadrozny

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Sánchez, D., Melin, P., Castillo, O. (2015). Fuzzy System Optimization Using a Hierarchical Genetic Algorithm Applied to Pattern Recognition. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_62

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11310-4_62

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11309-8

  • Online ISBN: 978-3-319-11310-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation