Evaluation of neural network variable influence measures for process control

Engineering Applications of Artificial Intelligence

Volumn 24, Issue 5, 2011, Pages 803-812

  Zobel, Christopher W ^a   Cook, Deborah F ^a  

^a PAMPLIN COLLEGE OF BUSINESS   (United States)

Author keywords

Dynamic control; Influence measures; Neural networks; Process control; Variable influence measures

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DECISION MAKERS; DYNAMIC CONTROLS; DYNAMIC PROCESS CONTROL; INFLUENCE MEASURES; INPUT PARAMETER; MANUFACTURING PROCESS; NETWORK VARIABLES; NETWORK-BASED; PROCESS OUTPUT; PROCESS PERFORMANCE; RELATIVE IMPACT; TO EFFECT; TRAINED NEURAL NETWORKS; VARIABLE INFLUENCE MEASURES;

DECISION MAKING; NEURAL NETWORKS;

PROCESS CONTROL;

EID: 79956103123     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2011.03.001     Document Type: Article

References (21)

1. F. Andersson, M. Åberg, and S. Jacobsson Algorithmic approaches for studies of variable influence, contribution and selection in neural networks Chemometrics and Intelligent Laboratory Systems 51 1 2000 61 72 (Pubitemid 30251930)
2. R. Andrews, J. Diederich, and A.B. Tickle Survey and critique of techniques for extracting rules from trained artificial neural networks Knowledge-based systems 8 6 1995 373 389
3. B. Baesens, R. Stiono, C. Mues, and J. Vanthienen Using neural network rule extraction and decision tables for credit risk evaluation Management Science. 49 3 2003 312 329
4. P. Brierley Tiberius for Excel 2003
5. S.I. Gallant Connectionist expert systems Communications of the ACM 31 2 1988 152 169
6. G.D. Garson Interpreting neural network connection strengths AI Expert 6 4 1991 46 52
7. N. Gilbert Explanation and dialogue Knowledge Engineering Review 4 3 1989 235 247
8. Hashem, S.; 1992. Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions, Proceedings of the 1992 International Joint Conference on Neural Networks. IEEE, Baltimore, MD, pp. 419424.
9. P. Howes, and N. Crook Using input parameter influences to support the decisions of feedforward neural networks Neurocomputing 24 13 1999 191 206 (Pubitemid 29098854)
10. M. Lam Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis Decision Support Systems 37 4 2004 567 581
11. B. Mak, and R.W. Blanning An empirical measure of element contribution in neural networks IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews 28 4 1998 561 564 (Pubitemid 128748656)
12. B. Mak, and T. Munakata Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3 European Journal of Operational Research 136 1 2002 212 229 (Pubitemid 33018813)
13. Moore, J.D.; Swartout, W.R.; 1989. A reactive approach to explanation, 11th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, Detroit, MI, pp. 15041510.
14. L. Nord, and S. Jacobsson A novel method for examination of the variable contribution to computational neural network models Chemometrics and Intelligent Laboratory Systems 44 12 1998 153 160 (Pubitemid 29016093)
15. S. Papadokonstantakis, A. Lygeros, and S.P. Jacobsson Comparison of recent methods for inference of variable influence in neural networks Neural Networks 19 4 2006 500 513 (Pubitemid 43850235)
16. R. Setiono, Y.L. James, and C.S. Yap Symbolic rule extraction from neural networks: An application to identifying organizations adopting IT Information and Management 34 2 1998 91 101 (Pubitemid 128419512)
17. K.Y. Tam, and M.Y. Kiang Managerial applications of neural networks: The case of bank failure predictions Management Science 38 7 1992 926 947
18. U.o. Toronto Delve Datasets 2006 University of Toronto Toronto, Canada
19. Y.; Yang, and Q. Zhang A hierarchical analysis for rock engineering using artificial neural networks Rock Mechanics and Rock Engineering 30 4 1997 207 222
20. I. Yeh Modeling slump flow of concrete using second-order regressions and artificial neural networks Cement and Concrete Composites 29 6 2007 474 480 (Pubitemid 46679230)
21. Y. Yoon, T. Guimaraes, and G. Swales Integrating artificial neural networks with rule-based expert systems Decision Support Systems 11 5 1994 497 507