Skip to main content
SpringerLink
Log in
Book cover

Computational Intelligence pp 1–35Cite as

An Introduction to Computational Intelligence

  • Chapter

Abstract

The chapter provides an introduction to computational intelligence. It begins with a thorough review of the underlying principles of artificial intelligence, and examines the scope of computational intelligence in overcoming the limitations of the traditional AI. The chapter then briefly introduces various tools of computational intelligence such as fuzzy logic, neural network, genetic algorithm, belief network, chaos theory, computational learning theory and artificial life. The synergistic behavior of the above tools on many occasions far exceeds their individual performance. A discussion on the synergistic behavior of neuro-fuzzy, neuro-GA, neuro-belief and fuzzy-belief network models is also included in the chapter. A list of tutorial problems is appended at the end of the chapter to build up students’ ability in handling real world problems.

This is a preview of subscription content, 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   99.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   129.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abidi, M. A. and Gonzalez, R. C. (Eds.), Data Fusion in Robotics and Machine Intelligence, Academic Press, San Diego, CA, 1992.

    MATH  Google Scholar 

  2. Anderson, J. A., “A simple neural network generating an associative memory,” Mathematical Biosciences, vol. 14, pp. 197–220, 1972.

    Article  MATH  Google Scholar 

  3. Antoniou, G., Nonmonotonic Reasoning, MIT Press, 1997.

    Google Scholar 

  4. Back, T., Fogel, D. B. and Michalewicz, Z., Handbook of Evolutionary Computation, IOP and Oxford University Press, Bristol, UK, 1997.

    Book  Google Scholar 

  5. Bender, E. A., Mathematical Methods in Artificial Intelligence, IEEE Computer Society Press, Los Alamitos, pp. 589–593, 1996.

    MATH  Google Scholar 

  6. Bezdek, J. C., Fuzzy Mathematics in Pattern Classification, Ph.D. thesis, Applied Mathematics Center, Cornell University, Ithaca, 1973.

    Google Scholar 

  7. Bezdek, J. C., “What is Computational Intelligence?” In Computational Intelligence Imitating Life, Zurada, J. M., Marks, R. J. and Robinson, C. J. (Eds.), IEEE Press, NY, pp. 1–12, 1994.

    Google Scholar 

  8. Biswas, B., Konar, A. and Mukherjee, A. K., “Image Matching with Fuzzy Moment Descriptors,” Engineering Applications of Artificial Intelligence, vol. 14, pp. 43–49, 2001.

    Article  Google Scholar 

  9. Broida, T. J., Kinematic and Statistical Models for Data Fusion Using Kalman Filtering, In Data Fusion in Robotics and Machine Intelligence, Abidi, M. A. and Gonzalez, R. C. (Eds.), Academic Press, San Diego, CA, 1992.

    Google Scholar 

  10. Carpenter, G. A. and Grossberg, S., “A massively parallel architecture for a self-organizing neural pattern recognition machine,” Computer Vision, Graphics and Image Processing, vol. 37, pp. 54–115, 1987.

    Article  Google Scholar 

  11. Carpenter, G. A. and Grossberg, S., “ART2: Self-organization of stable category recognition codes for analog input patterns,” Applied Optics, vol. 23, pp. 4919–4930, Dec. 1987.

    Article  Google Scholar 

  12. Castro, J. L., Carlos, M. J., and Benitez, J. M., “Interpretation of Artificial Neural Networks by Means of Fuzzy Rules,” IEEE Trans. on Neural Networks, vol. 13, no. 1, Jan. 2002.

    Google Scholar 

  13. Chakraborty, U. K. and Dastidar, D. G., “Using reliability analysis to estimate the number of generations to convergence in genetic algorithm,” Information Processing Letters, vol. 46, pp. 199–209, 1993.

    Article  MATH  MathSciNet  Google Scholar 

  14. Chakraborty, U. K. and Muehlenbein, H., “Linkage equilibrium and genetic algorithms,” Proc. 4 th IEEE Int. Conf. on Evolutionary Computation, Indianapolis, pp. 25–29, 1997.

    Google Scholar 

  15. Chakraborty, U. K., Deb, K. and Chakraborty, M., “Analysis of selection algorithms: A Markov chain approach,” Evolutionary Computation, vol. 4, no. 2, pp. 133–167, 1996.

    Article  Google Scholar 

  16. Davis, T. E. and Principa, J. C., “A Markov chain framework for the simple Genetic Algorithm,” Evolutionary Computation, vol. 1, no. 3, pp. 269–288, 1993.

    Article  Google Scholar 

  17. De Jong, K. A., An analysis of behavior of a class of genetic adaptive systems, Doctoral dissertation, University of Michigan, 1975.

    Google Scholar 

  18. Elfes, A., Multi-source Spatial Fusion Using Bayesian Reasoning, In Data Fusion in Robotics and Machine Intelligence, Abidi, M. A. and Gonzalez, R. C. (Eds.), Academic Press, San Diego, CA, 1992.

    Google Scholar 

  19. Filho, J. L. R. and Treleven, P. C., Genetic Algorithm Programming Environment, IEEE Computer Society Press, pp. 28–43, June 1994.

    Google Scholar 

  20. Fogel, D. B., Evolutionary Computation, IEEE Press, Piscataway, NJ, 1995.

    Google Scholar 

  21. Fu, Li M, Neural Networks in Computer Intelligence, McGraw-Hill, NY, 1994.

    Google Scholar 

  22. Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989.

    MATH  Google Scholar 

  23. Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice-Hall, NJ, 1999.

    MATH  Google Scholar 

  24. Hertz, J., Krogn, A. and Palmer, G. R., Introduction to the Theory of Neural Computation, Addison-Wesley, Reading, MA, 1990.

    Google Scholar 

  25. Holland, J. H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.

    Google Scholar 

  26. Hopfield, J. J., “Neural networks with graded response have collective computational properties like those of two state neurons,” Proc. of the National Academy of Sciences, vol. 81, pp. 3088–3092, May 1984.

    Article  Google Scholar 

  27. Hopfield, J. “Neural nets and physical systems with emergent collective computational abilities,” Proc. of the National Academy of Sciences, vol. 79, pp. 2554–2558, 1982.

    Article  MathSciNet  Google Scholar 

  28. Huntsberger, T. L., Data Fusion: A Neural Network Implementation, In Data Fusion in Robotics and machine Intelligence, Abidi, M. A. and Gonzalez, R. C. (Eds.), Academic Press, San Diego, CA, 1992.

    Google Scholar 

  29. Jain, L. C. and Lazzerini, B. (Eds.), Knowledge-Based Intelligent Techniques in Character Recognition, CRC Press, Boca Raton, 1999.

    MATH  Google Scholar 

  30. Jain, L. C. and Martin, N. M. (Eds.), Fusion of Neural Networks, Fuzzy Sets and Genetic Algorithms: Industry Applications, CRC Press, 1998.

    Google Scholar 

  31. Jain, L. C. and Silva, C. W. De. (Eds.), Intelligent Adaptive Control: Industrial Applications, CRC press, Boca Raton, 1998.

    Google Scholar 

  32. Jain, L. C. (Ed.), Intelligent Biometric Techniques in Fingerprint and Face Recognition, CRC Press, Boca Raton, 1999.

    Google Scholar 

  33. Jamshidi, M., Titli, A., Zadeh, L. and Boverie, S. (Eds.), Applications of Fuzzy Logic: Towards High Machine Intelligence Quotient Systems, Prentice-Hall, Englewood Cliffs, NJ, 1997.

    Google Scholar 

  34. Klir, G. J. and Yuan, B., Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice-Hall, NJ, 1995.

    MATH  Google Scholar 

  35. Kohonen, T., Barna, G. and Chrisley, R., “Statistical pattern recognition using neural networks: Benchmarking studies,” IEEE Conf. on Neural Networks, San Diego, vol. 1, pp. 61–68.

    Google Scholar 

  36. Kohonen, T., Self-organization and Associative Memory, Springer-Verlag, Berlin, 1989.

    Google Scholar 

  37. Konar, A. and Jain, L. C., An introduction to computational intelligence paradigms, In Practical Applications of Computational Intelligence Techniques, Jain, L. C. and Wilde, P. D. (Eds.), Kluwer, 2001.

    Google Scholar 

  38. Konar, A. and Mandal, A. K., “Uncertainty Management in Expert Systems using Fuzzy Petri Nets,” IEEE Trans. on Knowledge and Data Engineering, vol. 8, no. 1, 1996.

    Google Scholar 

  39. Konar, A. and Pal, S., Modeling cognition with fuzzy neural nets, In Neural Network Systems: Techniques and Applications, Leondes, C. T. (Ed.), Academic Press, New York, pp. 1341–1391, 1999.

    Google Scholar 

  40. Konar, A., Artificial Intelligence and Soft Computing: Behavioral and Cognitive modeling of the Human Brain, CRC Press, Boca Raton, 1999.

    Google Scholar 

  41. Kosko, B., Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall, Englewood Cliffs, NJ, 1991.

    Google Scholar 

  42. Kosko, B., “Adaptive bi-directional associative memories,” Applied Optics, vol. 26, pp. 4947–4960, 1987.

    Article  Google Scholar 

  43. Kosko, B., “Bi-directional associative memories,” IEEE Trans. on Systems, Man and Cybernetics, vol. SMC-18, pp. 49–60, Jan 1988.

    Article  MathSciNet  Google Scholar 

  44. Koza, J. R., Genetic Programming: On the programming of Computers by Means of Natural Selection, MIT Press, 1992.

    Google Scholar 

  45. Langton, C. G. (Ed.), Artificial Life, vol. 6, Addison-Wesley, Reading, MA, 1989.

    Google Scholar 

  46. Luo, Fa-Long and Unbehauen, R., Applied Neural Networks for Signal Processing, Cambridge University Press, London, pp. 1–31, 1997.

    Google Scholar 

  47. Marks, R. J., “Intelligence: Computational versus Artificial,” IEEE Trans. on Neural Networks, vol. 4, pp. 737–739, 1993.

    Google Scholar 

  48. Mc Donell, J. R., “Control” in Handbook of Evolutionary Computation, Back, T., Fogel, D. B. and Michalewicz, Z. (Eds.), IOP and Oxford University Press, New York, 1998.

    Google Scholar 

  49. Michalewicz, Z, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin, 1992.

    MATH  Google Scholar 

  50. Mitchell, M. M., Machine Learning, McGraw-Hill, New York, pp. 81–127, 1997.

    MATH  Google Scholar 

  51. Mitchell, M., An introduction to Genetic Algorithms, MIT Press, Cambridge, MA, 1996.

    Google Scholar 

  52. Muehlenbein, H. and Chakraborty, U. K., “Gene pool recombination genetic algorithm and the onemax function,” Journal of Computing and Information Technology, vol. 5, no. 3, pp. 167–182, 1997.

    Google Scholar 

  53. Pal, S. K. and Mitra, S., Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing, John Wiley & Sons, Inc., 1999.

    Google Scholar 

  54. Patterson, D. W., Introduction to Artificial Intelligence and Expert Systems, Prentice-Hall, Englewood Cliffs, NJ, pp. 107–119, 1990.

    Google Scholar 

  55. Paul, B., Konar, A. and Mandal, A. K., “Fuzzy ADALINEs for gray image recognition,” Neurocomputing, vol. 24, pp. 207–223, 1999.

    Article  Google Scholar 

  56. Pearl, J., “Distributed revision of composite beliefs,” Artificial Intelligence, vol. 33, pp. 173–213, 1987.

    Article  MATH  MathSciNet  Google Scholar 

  57. Pearl, J., “Fusion, propagation and structuring in belief networks,” Artificial Intelligence, vol.29, pp. 241–288, 1986.

    Article  MATH  MathSciNet  Google Scholar 

  58. Pedrycz, W., Fuzzy Sets Engineering, CRC Press, Boca Raton, FL, pp. 73–106, 1996.

    Google Scholar 

  59. Pedrycz, W. and Gomide, F., An Introduction to Fuzzy Sets: Analysis and Design, MIT Press, 1998.

    Google Scholar 

  60. Peng, Y. and Reggia, J. A., “A probabilistic causal model for diagnostic problem solving,” IEEE Trans. on Systems, Man and Cybernetics, SMC-17, no. 3, pp. 395–408, May-June 1987.

    Article  Google Scholar 

  61. Quinlan, J. R., “Induction of decision trees,” Machine Learning, vol. 1, no. 1, pp. 81–106.

    Google Scholar 

  62. Ross, T. J., Fuzzy Logic with Engineering Applications, McGraw-Hill, 1995.

    Google Scholar 

  63. Rumelhart, D. E. and McClelland, J. L., Parallel Distributed Processing: Exploring in the microstructure of cognition, MIT Press, Cambridge, MA, 1986.

    Google Scholar 

  64. Rumelhart, D. E., Hinton, G. E. and Williams R. J., “Learning representations by back-propagation errors,” Nature, vol. 323, pp. 533–536, 1986.

    Article  Google Scholar 

  65. Russel, S. and Norvig, P., Artificial Intelligence: A Modern Approach, Prentice-Hall, Englewood Cliffs, NJ, pp. 598–644, 1995.

    Google Scholar 

  66. Saha, P. and Konar, A., “A heuristic approach for computing the max-min inverse fuzzy relation,” Int. J. of Approximate Reasoning, vol. 30, pp. 131–147, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  67. Schalkoff, R. J., Artificial Neural Networks, McGraw-Hill, New York, pp. 146–188, 1997.

    MATH  Google Scholar 

  68. Shafer, G. and Logan, R., “Implementing Dempster’s rule for hierarchical evidence,” Artificial Intelligence, vol. 33, pp. 271–298, 1987.

    Article  MATH  MathSciNet  Google Scholar 

  69. Shafer, G., A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, 1976.

    MATH  Google Scholar 

  70. Shenoy, P. P. and Shafer, G., “Propagating belief functions with local computations,” IEEE Expert, pp. 43–52, Fall 1986.

    Google Scholar 

  71. Shoham, Y., Artificial Intelligence Techniques in PROLOG, Morgan Kaufmann, San Mateo, CA, pp. 183–185, 1994.

    MATH  Google Scholar 

  72. Shortliffe, E. H. and Buchanan, B. G., “A model of inexact reasoning,” Mathematical Biosciences, vol. 23, pp. 351–379, 1975.

    Article  MathSciNet  Google Scholar 

  73. Shortliffe, E. H., Computer based medical consultations: MYCIN, American Elsevier, New York, 1976.

    Google Scholar 

  74. Sil, J. and Konar, A., “Reasoning Using a Probabilistic Predicate Transition Net Model,” Int. J. of Modeling and Simulations, vol. 21, no. 2, pp. 155–168, 2001.

    Google Scholar 

  75. Srinivas, M. and Patnaik, L. M., “Genetic search: Analysis using fitness moments,” IEEE Trans. on Knowledge and Data Engg., vol. 8, no. 1, pp. 120–133, 1996.

    Article  Google Scholar 

  76. Tank, D. W. and Hopfield, J. J., “Simple neural optimization networks: An A/D converter, signal decision circuit and a linear programming circuit,” IEEE Trans. on Circuits and Systems, vol. 33, pp. 533–541, 1986.

    Article  Google Scholar 

  77. Teodorescu, H. N., Kandel, A. and Jain, L. C., Eds., Fuzzy and Neuro-Fuzzy systems in Medicine, CRC Press, London, 1999.

    Google Scholar 

  78. Vose, M. D. and Liepins, G. E., “Punctuated equilibrium in genetic search,” Complex Systems, vol. 5, pp. 31–44, 1991.

    MATH  MathSciNet  Google Scholar 

  79. Vose, M. D., Genetic Algorithms, MIT Press, 1999.

    Google Scholar 

  80. Wasserrman, P. D., Neural Computing: Theory and Practice, Van Nostrand Reinhold, New York, pp. 49–85, 1989.

    Google Scholar 

  81. Widrow, B. and Hoff, M, E., Adaptive Switching Circuits, in 1960 IRE WESCON Convention Record, Part 4, pp. 96–104, NY, 1960.

    Google Scholar 

  82. Widrow, B., “Generalization and information storage in networks of ADALINE neurons,” in Self-organizing systems, Yovits, M. C., Jacobi, G. T. and Goldstein, G. D., Eds., pp. 435–461, 1962.

    Google Scholar 

  83. Williams, R. J., “On the use of back-propagation in associative reinforcement learning,” in IEEE Int. Conf. on Neural Networks, NY, vol. 1, pp. 263–270, 1988.

    Article  Google Scholar 

  84. Williams, R. J. and Peng, J., “Reinforcement learning algorithm as function optimization,” Proc. of Int. Joint Conf. on Neural Networks, NY, vol. II, pp. 89–95, 1989.

    Article  Google Scholar 

  85. Winston, P., Learning Structural Descriptions from Examples, Ph.D. Dissertation, MIT Technical Report AI-TR-231, 1970.

    Google Scholar 

  86. Zadeh, L. A., “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Trans. Systems, Man and Cybernetics, vol. 3, pp. 28–45, 1973.

    MATH  MathSciNet  Google Scholar 

  87. Zadeh, L. A., Fuzzy Sets, Information and Control, vol. 8, pp. 338–353, 1965.

    MATH  MathSciNet  Google Scholar 

  88. Zadeh, L. A., Fuzzy logic, neural networks and soft computing, one page course announcement of CS 294-4, Spring 1993, University of California at Berkeley, November 1992.

    Google Scholar 

  89. Zimmerman, H. J., Fuzzy Set Theory and Its Applications, Kluwer Academic, Dordrecht, The Netherlands, pp. 131–162, 1996.

    Google Scholar 

Download references

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

(2005). An Introduction to Computational Intelligence. In: Computational Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27335-2_1

Download citation

  • DOI: https://doi.org/10.1007/3-540-27335-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20898-3

  • Online ISBN: 978-3-540-27335-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation