Analyzing mathematical models with inductive learning networks

European Journal of Operational Research

Volumn 93, Issue 2, 1996, Pages 387-401

  Steiger, David M ^a   Sharda, Ramesh ^b  

^a UNIVERSITY OF NORTH CAROLINA AT GREENSBORO   (United States)

^b OKLAHOMA STATE UNIVERSITY   (United States)

Author keywords

Decision support systems; Group method of data handling (GMDH); Inductive learning networks; Model analysis; Neural networks

Indexed keywords

BACKPROPAGATION; DATA HANDLING; DECISION SUPPORT SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION;

BACKPROPAGATION NEURAL NETWORKS; GROUP METHOD OF DATA HANDLING; INDUCTIVE LEARNING NETWORKS; MODEL ANALYSIS;

NEURAL NETWORKS;

EID: 0030572670     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/0377-2217(96)00036-7     Document Type: Article

References (43)

1. AbTech (1990), Abductory Inductive Models - User's Manual, Ab-Tech, Inc., Charlottesville, VA.
2. Barron, A.R. (1984), "Predicted square error: A criterion for automatic model selection", in: S.J. Farlow (Ed.), Self-Organizing Methods in Modeling: GMDH Type Algorithms, Marcel Dekker, New York, 86-104.
3. Barron, R.L., Mucciardi, A.N., Cook, F.J., Barron, A.R. and Craig, J.N. (1984), "Adaptive learning in networks: Development and applications in the U.S. of algorithms related to GMDH", in: S.J. Farlow (Ed.), Self-Organizing Methods in Modeling: GMDH Type Algorithms, Marcel Dekker, New York, 25-66.
4. Brennan, J.J. and Elam, J.J. (1986), "Understanding and validating results in model-based decision support systems", Decision Support Systems 2, 49-54.
5. Dayhoff, J.E. (1990), Neural Network Architectures, Von Nostrand Reinhold, New York.
6. Donahue, R.L. and Spearman, M.L. (1993), "Improving the design of stochastic production lines: an approach using perturbation analysis", International Journal of Production Research 31/12, 2789-2806.
7. Elam, J.J. and Konsynski, B. (1987), "Using artificial intelligence techniques to enhance the capabilities of model management systems", Decision Sciences 18/3, 487-501.
8. EXECUCOM (1992), Interactive Financial Planning System: User's Manual. EXECUCOM, Austin, TX.
9. Farlow, S.J. (Ed.) (1984), Self-Organizing Methods in Modeling: GMDH type Algorithms, Marcel Dekker, New York.
10. Geoffrion, A.M. (1976), "The purpose of mathematical programming is insight, not numbers", Interfaces 7/1, 81-92.
11. Geoffrion, A.M. (1987), "An introduction to structured modeling", Management Science 33/5, 547-588.
12. Greenberg, H.J. (1982), "A tutorial on computer-assisted analysis" , in: Greenberg, Murphy and Shaw (Eds.) Advanced Techniques in the Practice of Operations Research, Elsevier Science, New York, 212-249.
13. Greenberg, H.J. (1990), "A primer of ANALYZE", Working Paper, University of Colorado at Denver, June.
14. Greenberg, H.J. (1993), "Enhancements of ANALYZE: A Computer-Assisted Analysis System for Linear Programming", ACM Transactions on Mathematical Software 19/2 223-256.
15. Greenberg, H.J. (1994), "Syntax-directed report writing in linear programming using ANALYZE", European Journal of Operational Research 72, 300-311.
16. Hamming, R.W. (1962), Numerical Methods for Scientists and Engineers, McGraw-Hill, New York, v.
17. Hopfield, J. and Tank, D. (1985), "Simple 'neural' optimization networks: An A/D converter, signal decision circuit and a linear programming circuit", Cybernetics 5, 141-152.
18. Ivakhnenko, A.G. (1971), "Polynomial theory of complex systems", IEEE Transactions on Systems, Man and Cybernetics 4, 364-384.
19. Ivakhnenko, A.G. (1984), "Past, present and future of GMDH", in: S.J. Farlow (Ed) Self-Organizing Methods in Modeling: GMDH Type Algorithms, Marcel Dekker, New York, 105-119.
20. Ivakhnenko, A.G., Krotov, G.I. and Visotsky, V.N. (1979), "Identification of the mathematical model of a complex system by the self-organization method", in: E.F. Halfon (Ed.), Theoretical Systems Ecology, Academic Press, New York.
21. Jones, C.V. (1992), "User interfaces and operations research", in E.G. Coffman, J.K. Lenstra and A.Y. Kan (Eds.), Handbook of Operations Research, Vol. 13, North-Holland, Amsterdam, 603-668.
22. Kimbrough, S.O., Moore, S.A., Pritchett, C.W. and Sherman, C.A. (1992), "On DSS support for candle-lighting analysis, Transactions of DSS-92, 118-135.
23. Kimbrough, S.O., Oliver, J.R. and Prichett, C.W. (1993), "On post-evaluation analysis: Candle-lighting and surrogate models", Interfaces 23/3, 17-28.
24. Kimbrough, S.O., Pritchett, C.W., Bieber, M.P.L. and Bhargava, H.K. (1990), "The Coast Guard's KSS project, Interfaces 20/6,5-16.
25. Kimbrough, S.O. and Oliver, F.R. (1994), "On automating candle-lighting analysis: Insight from search with genetic algorithm and approximate models", Proceedings of the 27th Annual Hawaii International Conference on System Sciences, 536-544.
26. Koltai, T., Larraneta, J. and Onieva, L. (1993), "Examination of the sensitivity of an operation schedule with perturbation analysis", International Journal of Production Research 31/12, 2777-2787.
27. Kosy, D.W. and Wise, B.P. (1984), "Self-explanatory financial planning models", Proceedings of the National Conference of Artificial Intelligence, August, 176-181.
28. Kosy, D.W. and Wise, B.P. (1986), "Overview of ROME: A Reason-Oriented Modeling Environment", in: L.F. Psu, (Ed.), Artificial Intelligence in Economics and Management, North-Holland, Amsterdam, 21-30.
29. Kurator, W.G. and O'Neill, R.P. (1980), "PERUSE: An interactive system for mathematical programs", ACM Transactions on Mathematical Software 6/4, 489-509.
30. Looi, C.-K. (1992), "Neural network methods in combinatorial optimization", Computers and Operations Research 19/3-4, 191-208.
31. Prager, M.H. (1988), "Group method of data handling: A new method for stock identification", Transactions of the American Fishery Society 117, 290-296.
32. Reddy, Y.V. (1985), "The role of introspective simulation in managerial decision making", DSS-85 Transactions, IADSS, University of Texas at Austin, 18-32.
33. Saltelli, A. and Homma, T. (1992), "Sensitivity analysis for model output", Computational Statistics and Data Analysis 13, 73-94.
34. Saltelli, A. and Marivoet, J. (1990), "Non-parametric statistics in sensitivity analysis for model output: A comparison of selected techniques", Reliability Engineering and Systems Safety 28, 229-253.
35. Savage, S.L. (1992), "What's Best: The ABC's of Optimization, Lindo Systems, Chicago, IL.
36. Sharda, R. and Steiger, D.M. (1995a), "Using artificial intelligence to enhance model analysis", in: Nash and Sofer (Eds.), The Impact of Emerging Technologies on Computer Science and Operations Research, Kluwer Academic Publishers, Dordrecht, 263-280.
37. Sharda, R. and Steiger, D.M. (1995b), "Inductive model analysis systems: Enhancing model analysis in decision support systems", Information Systems Research (to appear).
38. Talon Development Corporation (1992), @BRAIN Neural Network Development System: User's Guide, Milwaukee, WI.
39. Tank, D. and Hopfield, J. (1986), "Simple 'neural' optimization networks: An A/D converter, signal decision circuit and a linear programming circuit", IEEE Transactions on Circuits and Systems CAS-33, 533-541.
40. Vaithyanathan, S. and Ignizio, J.P. (1992), "A stochastic neural network for resource constrained scheduling", Computers and Operations Research 19/3-4, 241-254.
41. Wagner, H.M. (1993), "Global sensitivity analysis", Working Paper, University of North Carolina at Chapel Hill, 1993.
42. Wang, R. and Chankong, V. (1992), "Recurrent neural networks for linear programming: Analysis and design principles", Computers and Operations Research 19/3-4, 297-311.
43. Wise, B.P. and Kosy, D.W. (1986), "Model-based evaluation of long-range resource allocation plans", in: L.F. Pau (Ed.), Artificial Intelligence in Economics and Management, North-Holland, Amsterdam, 93-102.