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An Introduction to Neural Networks and a Comparison with Artificial Intelligence and Expert Systems

Author

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  • Fatemeh Zahedi

    (Management Sciences Department, University of Massachusetts-Boston, Boston, Massachusetts 02125)

Abstract

Artificial intelligence (including expert systems) (AI/ES) and neural networks (NN) provide methods for formalizing qualitative aspects of business systems. They complement quantitative methods in solving business problems. While AI and NN have the common goal of simulating human intelligence, they use different methods. AI/ES assumes the brain is a black box and imitates the human reasoning process. It processes knowledge sequentially, represents it explicitly, and mostly uses deductive reasoning. Learning takes place outside the system.NN treats the brain as a white box and imitates its structure and function, using a parallel approach to simulate human intelligence. It represents knowledge implicitly within its structure and applies inductive reasoning to process knowledge. Learning takes place within the system. Both AI/ES and NN have great potential to solve qualitative problems, and their integration could provide a powerful tool for dealing with problems outside the domain of current problem-solving methods.

Suggested Citation

  • Fatemeh Zahedi, 1991. "An Introduction to Neural Networks and a Comparison with Artificial Intelligence and Expert Systems," Interfaces, INFORMS, vol. 21(2), pages 25-38, April.
    • Handle: RePEc:inm:orinte:v:21:y:1991:i:2:p:25-38
    DOI: 10.1287/inte.21.2.25
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    Cited by:

    1. Palocsay, Susan W. & Stevens, Scott P. & Brookshire, Robert G. & Sacco, William J. & Copes, Wayne S. & Buckman, Robert F. & Smith, J. Stanley, 1996. "Using neural networks for trauma outcome evaluation," European Journal of Operational Research, Elsevier, vol. 93(2), pages 369-386, September.
    2. Caputo, Antonio C. & Pelagagge, Pacifico M., 2008. "Parametric and neural methods for cost estimation of process vessels," International Journal of Production Economics, Elsevier, vol. 112(2), pages 934-954, April.
    3. Daniela Carlucci & Paolo Renna & Giovanni Schiuma, 2013. "Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network," Health Care Management Science, Springer, vol. 16(1), pages 37-44, March.
    4. Klein, B. D. & Rossin, D. F., 1999. "Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy," Omega, Elsevier, vol. 27(5), pages 569-582, October.
    5. Sabuncuoglu, Ihsan & Gurgun, Burckaan, 1996. "A neural network model for scheduling problems," European Journal of Operational Research, Elsevier, vol. 93(2), pages 288-299, September.
    6. Chen, S. K. & Mangiameli, P. & West, D., 1995. "The comparative ability of self-organizing neural networks to define cluster structure," Omega, Elsevier, vol. 23(3), pages 271-279, June.

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