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Research Problems for Graduate Thesis and Pre-Ph D Preparatory Courses

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Computational Intelligence

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Abstract

This chapter addresses selected research problems in computational intelligence. The problems are introduced informally so that anyone without any background in the specific domain easily understands them. The problems require either a mathematical formulation or a computer simulation for their solutions. An outline to the solution of the problems is also suggested.

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References

  1. Adriaans, P. and Zantinge, D., Data Mining, Addison-Wesley, Singapore, 1999.

    Google Scholar 

  2. Biswas, B. and Konar, A., “Speaker identification from voice using neural networks,” J. of Scientific & Industrial Research, vol. 61, pp. 599–606, 2002.

    Google Scholar 

  3. Biswas, B., Fuzzy Decision Support System for Criminal Investigation, Ph.D. Thesis, Jadavpur University, 2001.

    Google Scholar 

  4. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P. and Uthurusamy, R. (Eds.), Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, Cambridge, 1996.

    Google Scholar 

  5. Fiesler, E. and Beale, R. (Eds.), Handbook of Neural Computation, Part G: Neural Network in Practice, IOP Publishing and Oxford University Press, 1997.

    Google Scholar 

  6. Fu, L. M., Neural Networks in Computer Intelligence, McGraw-Hill, Singapore, 1994.

    Google Scholar 

  7. Jain, L. C., Halici, U., Hayashi, L., Lee, S. B. and Tsutsui, S., (Eds.), Intelligent Biometric Techniques in Fingerprint and Face Recognition, CRC Press, Boca Raton, Florida, 1999.

    Google Scholar 

  8. Klir, G. J. and Yuan, B., Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice-Hall, NJ, pp. 418–441, 1995.

    MATH  Google Scholar 

  9. Konar, A. and Pal, S., “Modeling Cognition with Fuzzy Neural Nets,” In Fuzzy Theory Systems: Techniques and Applications, Leondes, C. T. (Ed.), Academic Press, pp. 1341–1391, 1999.

    Google Scholar 

  10. Krishnapuram, R. K., Segmentation, In Handbook of Fuzzy Computation, Ruspini, E. H., Bonissone, P. P. and Pedrycz, W. (Eds.), IOP Publishing, 1998.

    Google Scholar 

  11. Lehninger, A. L., Biochemistry: The Molecular Basis of Cell Structure and Function, Worth Publishers, Inc. NY, pp. 67–108, 1970.

    Google Scholar 

  12. Lesk, A. M., Introduction to Bioinformatics, Oxford University Press, NY, pp. 242–246, 2002

    Google Scholar 

  13. Majumder, P. and Tudnick, E. M., Genetic Algorithms for VLSI Design, Layout and Test Automation, Prentice-Hall PTR, NJ, 1999.

    Google Scholar 

  14. Pal, S., Cognitive Modeling Using Fuzzy Logic Network, Ph. D. Thesis, Jadavpur University, 2002.

    Google Scholar 

  15. Saha, P. and Konar, A., “A heuristic algorithm for computing the maxmin inverse fuzzy relation,” Int. J. of Approximate Reasoning, Elsevier, vol. 30, pp. 131–147, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  16. Saha, P., Abductive Reasoning with Inverse Discrete Fuzzy Relations, Ph.D. Thesis, Jadavpur University, 2001.

    Google Scholar 

  17. Saha, P. and Konar, A., “Reciprocity and Duality in a Fuzzy Petri Net,” Int. J. of Modeling and Simulation, vol. 24, no. 3, 2004.

    Google Scholar 

  18. Silberschatz, A., Korth, H. F. and Sudarshan, S., Database System Concepts, McGraw-Hill, Singapore, pp. 702–710, 1997.

    MATH  Google Scholar 

  19. Solo, V. and Kong, X., Adaptive Signal Processing Algorithms: Stability and Performance, Prentice-Hall, NJ, 1975.

    Google Scholar 

  20. Widrow, B. and Winter, R., “Neural nets for adaptive filtering and adaptive pattern recognition,” IEEE Computer, Special Issue on Artificial Neural Systems, pp. 25–39, March 1988.

    Google Scholar 

  21. Widrow, B. and Stearns, S. D., Adaptive Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, pp. 193–404, 1985.

    MATH  Google Scholar 

  22. Yuhas, B. P., Goldstein, M. H., Sejnowski, T. J. and Jenkins, R. E., “Neural network models of sensory integration for improved vowel recognition,” Proc. IEEE, vol. 78, no. 10, Oct. pp. 1658–1668, 1990. Also appeared in Neural Networks: Theoretical Foundations and Analysis, Lau, C. (Ed.), IEEE Press, pp.311–320, 1992.

    Article  Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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(2005). Research Problems for Graduate Thesis and Pre-Ph D Preparatory Courses. In: Computational Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27335-2_23

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  • DOI: https://doi.org/10.1007/3-540-27335-2_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20898-3

  • Online ISBN: 978-3-540-27335-6

  • eBook Packages: EngineeringEngineering (R0)

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