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AFSS International Conference on Fuzzy Systems

AFSS 2002: Advances in Soft Computing — AFSS 2002 pp 274–280Cite as

Evolutionary Subsethood Product Fuzzy Neural Network

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2275))

Abstract

This paper employs a simple genetic algorithm (GA) to search for an optimal set of parameters for a novel subsethood product fuzzy neural network introduced elsewhere, and to demonstrate the pattern classification capabilities of the network. The search problem has been formulated as an optimization problem with an objective to maximize the number of correctly classified patterns. The performance of the network, with GA evolved parameters, is evaluated by computer simulations on Ripley’s synthetic two class data. The network performed excellently by being at par with the Bayes optimal classifier, giving the best possible error rate of 8%. The evolutionary subsethood product network outperformed all other models with just two rules.

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References

  1. Mitra, S., Hayashi, Y.: Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Transactions on Neural Networks 11 (May 2000) 748–768

    Google Scholar 

  2. Kasabov, N.: Neuro-fuzzy techniques for intelligent processing. Physica Verlag (1999)

    Google Scholar 

  3. Fuller, R.: Introduction to neuro-fuzzy systems. Heidelberg New York: Physica-Verlag (2000)

    MATH  Google Scholar 

  4. Lin, C., Lee, C.S.G.: Neural fuzzy systems: A neuro-fuzzy synergism to intelligent systems. Upper Saddle River NJ: Prentice Hall P T R (1996)

    Google Scholar 

  5. Bezdek, J.C., Keller, K., Krishnapuram, R., Pal, N.R.: Fuzzy models and algorithms for pattern recognition and image processing. Kluwer Academic Press (1999)

    Google Scholar 

  6. Kim, J., Kasabov, N.: HyFIS: Adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. IEEE Transactions on Neural Networks 12(9) (1999) 1301–1321

    Google Scholar 

  7. Mitra, S., Pal, S.: Fuzzy multi-layer perceptron, inferencing and rule generation. IEEE Transactions on Neural Networks (1995)

    Google Scholar 

  8. Belarbi, K., Titel, F.: Genetic algorithm for the design of a class of fuzzy controllers: An alternative approach. IEEE Transactions on Fuzzy Systems 8 (August 2000) 398–405

    Google Scholar 

  9. Pujol, J.C.F., Poli, R.: Evolving the topology and the weights of neural networks using a dual representation. Applied Intelligence 8(1) (1998) 73–84

    Article  Google Scholar 

  10. Russo, M.: FuGeNeSys-A fuzzy genetic neural system for fuzzy modeling. IEEE Transactions on Fuzzy Systems 6 (August 1998) 373–388

    Google Scholar 

  11. Russo, M.: Genetic fuzzy learning. IEEE Transactions on Evolutionary Computation 4 (September 2000) 259–273

    Google Scholar 

  12. Russo, M.: Distributed fuzzy learning using the MULTISOFT machine. IEEE Transactions on Neural Networks 12(3) (May 2001) 475–484

    Article  Google Scholar 

  13. Paul, S., Kumar, S.: Adaptive rule-based linguistic networks for function approximation. 4th International Conference on Advances in Pattern Recognition and Digital Techniques N. R. Pal, A. K. De, and J. Das, eds., Indian Statistical Institute Calcutta Narosa Publishing House (December 1999) 246–250

    Google Scholar 

  14. Paul, S., Kumar, S.: Subsethood based adaptive linguistic networks for pattern classification. IEEE Transactions on Systems, Man and Cybernetics, to appear.

    Google Scholar 

  15. Paul, S., Kumar, S.: Fuzzy neural inference system using mutual subsethood products with applications in medical diagnosis and control. Proceedings IEEE 10th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Melbourne, Australia, December 2–5 2001

    Google Scholar 

  16. Ripley, B.: Pattern recognition and neural networks. Cambridge University Press (1996)

    Google Scholar 

  17. Kosko, B.: Fuzzy engineering Englewood Cliffs: Prentice Hall (1997)

    Google Scholar 

  18. Goldberg, D. E.: Genetic algorithms in search, optimization and machine learning Reading, MA: Addison Wesley (1989)

    Google Scholar 

  19. Cherkassky, V., Mulier, F.: Learning from data: concepts, theory, and methods.John Wiley and Sons (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Physics and Computer Science Faculty of Science, Dayalbagh Educational Institute, 282005, Dayalbagh, Agra, India

    C. Shunmuga Velayutham & Satish Kumar

  2. Dept. of Electrical Engineering Technical College, Dayalbagh Educational Institute, 282005, Dayalbagh, Agra, India

    Sandeep Paul

Authors
  1. C. Shunmuga Velayutham
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  2. Sandeep Paul
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  3. Satish Kumar
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Editor information

Editors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, 203 B.T. Road, 700108, Calcutta, India

    Nikhil R. Pal

  2. Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako, Japan

    Michio Sugeno

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© 2002 Springer-Verlag Berlin Heidelberg

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Velayutham, C.S., Paul, S., Kumar, S. (2002). Evolutionary Subsethood Product Fuzzy Neural Network. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_37

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  • DOI: https://doi.org/10.1007/3-540-45631-7_37

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43150-3

  • Online ISBN: 978-3-540-45631-5

  • eBook Packages: Springer Book Archive

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