nach oben

Neural Computing and Applications

Erschienen in:

01.04.2010 | Original Article

Using a heuristic approach to derive a grey-box model through an artificial neural network knowledge extraction technique

verfasst von: William A. Young II, Gary R. Weckman

Erschienen in: Neural Computing and Applications | Ausgabe 3/2010

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Artificial neural networks (ANNs) are primarily used in academia for their ability to model complex nonlinear systems. Though ANNs have been used to solve practical problems in industry, they are not typically used in nonacademic environments because they are not very well understood, complicated to implement, or have the reputation of being a “black-box” model. Few mathematical models exist that outperform ANNs. If a highly accurate model can be constructed, the knowledge should be used to understand and explain relationships in a system. Output surfaces can be analyzed in order to gain additional knowledge about a system being modeled. This paper presents a systematic approach to derive a “grey-box” model from the knowledge obtained from the ANN. A database for an automobile’s gas mileage performance is used as a case study for the proposed methodology. The results show a greater ability to generalize system behavior than other benchmarked methods.

Vorheriger Artikel Model reference controlled separately excited DC motor

Nächster Artikel Neural network-based simulation for response identification of two-storey shear building subject to earthquake motion

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Young W, Weckman G, Thompson J, Brown M (2008) Artificial neural networks for knowledge extraction of concrete shear strength prediction. Int J Ind Eng 15(1):1–10MATH

Hernandez S, Nesic S, Weckman G, Ghai V (2006) Use of artificial neural networks for predicting crude oil effect on CO₂ corrosion of carbon steels. Corros J 62(6):1–35

Snow A, Weckman G, Chayanam K (2006) Modeling telecommunication outages due to power loss. Int J Ind Eng Theory Appl Pract 13(1):51–60

Weckman G, Lakshminarayanan S, Snow A, Marvel J (2008) An integrated stock market forecasting model using neural networks. Int J Business Forecast Mark Intell 1(1). doi:10.1504/IJBFMI.2008.020813

Millie D, Weckman G, Paerl H, Pinckney J, Bendis B, Pigg R et al (2006) Neural network modeling of estuarine indicators: hindcasting phytoplankton biomass and net ecosystem production in the Neuse (North Carolina) and Trout (Florida) Rivers. Ecol Indic 6:589–608. doi:10.1016/j.ecolind.2005.08.021 CrossRef

Ozesmi S, Ozesmi U (1999) An artificial neural network approach to spatial habitat modeling with interspecific interaction. Ecol Model 116:15–31. doi:10.1016/S0304-3800(98)00149-5 CrossRef

Box G, Draper N (1987) Empirical model-building and response surfaces. Wiley, New YorkMATH

National Aeronautics Space Administration (2004) NASA cost estimation handbook. National Aeronautics and Space Administration, Washington, DC

Huysmans J, Baesens B, Vanthienen J (2006) Using rule extraction to improve the comprehensibility of predictive models. FETEW Internal Research. K.U.Leuven, Leuven

10.

Xiong Q, Jutan A (2002) Grey-box modelling and control of chemical processes. Chem Eng Sci 57:1027–1039. doi:10.1016/S0009-2509(01)00439-0 CrossRef

11.

Oussar Y, Dreyfus G (2001) How to be a gray box: dynamic semi-physical modeling. Neural Netw 14:1161–1172. doi:10.1016/S0893-6080(01)00096-X CrossRef

12.

Francisco C, Acuña G, Cubillos F (2007) Indirect training of grey-box models: application to a bioprocess. In: Cruz F, Acuña G, Cubillos F, Moreno V, Bassi D (eds) Lecture Notes in Computer Science. Springer, Berlin/Heidelberg, pp 391–397

13.

Acuña G, Cubillos F, Thibault J, Latrille E (1999) Comparison of methods for training grey-box neural network models. Comput Chem Eng Suppl 23:561–564CrossRef

14.

Johannet A, Vayssade B, Bertin D (2007) Neural networks: from black box toward transparent box application to evapotranspiration modeling. Proc World Acad Sci Eng Technol 24:162–169

15.

Tornøe C, Jacobsen J, Pedersen O, Hansen T, Madsen H (2004) Grey-box modelling of pharmacokinetic/pharmacodynamic systems. J Pharmacokinet Pharmacodyn 31(5):401–417. doi:10.1007/s10928-004-8323-8 CrossRef

16.

Geeraerd AH, Herremans CH, Cenens C, Van Impe JF (1998) Application of artificial neural networks as a non-linear modular modeling technique to describe bacterial growth in chilled food products. Int J Food Microbiol 44:49–68. doi:10.1016/S0168-1605(98)00127-5 CrossRef

17.

Reed RD, Marks RJ (1998) Neural smithing: supervised learning in feedforward artificial neural networks. MIT Press, Cambridge

18.

Hornik H, Stinchcomb M, White J (1982) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366. doi:10.1016/0893-6080(89)90020-8 CrossRef

19.

Meireles M, Almeida P, Simões M (2003) A comprehensive review for industrial applicability of artificial neural networks. IEEE Trans Ind Electron 50(3):585–601. doi:10.1109/TIE.2003.812470 CrossRef

20.

Lek S, Guegan J (1999) Artificial neural networks as a tool in ecological modeling, an introduction. Ecol Modell 120:65–73. doi:10.1016/S0304-3800(99)00092-7 CrossRef

21.

Lange N (1999) New mathematical approaches in hydrological modeling—an application of artificial neural networks. Phys Chem Earth Part B Hydrol Oceans Atmos 24(1):31–35. doi:10.1016/S1464-1909(98)00007-0 CrossRef

22.

Browne A, Hudson B, Whitley D, Ford M, Picton P, Kazemian H (2003) Knowledge extraction from neural networks. In: Proceedings of the 29th Annual Conference of the IEEE Industrial Electronics Society. Roanoke, Virginia, pp. 1909–1913

23.

Garson D (1991) Interpreting neural-network connection weights. AI Expert 6(4):47–51

24.

Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern 30(4):451–462. doi:10.1109/5326.897072 CrossRef

25.

Cristea A, Cristea P, Okamoto T (1997) Neural networks knowledge extraction. Electrotech Eng 42(2):477–491

26.

Towell G, Shavlik J (1993) The extraction of re¯ned rules from knowledge-based neural networks. Mach Learn 13(1):71–101

27.

Setiono R, Liu H (1997) Neurolinear: from neural networks to oblique decision rules. Neural Comput 17(1):1–24

28.

Setiono R, Leow W (2000) FERNN: an algorithm for fast extraction of rules from neural networks. Appl Intell 12(1–2):15–25. doi:10.1023/A:1008307919726 CrossRef

29.

Saito K, Nakano R (2002) Extracting regression rules from neural networks. Neural Netw 15(10):1279–1288. doi:10.1016/S0893-6080(02)00089-8 CrossRef

30.

Breiman L, Friedman H, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth, BeltnontMATH

31.

Quinlan J (1988) C4.5: programs for machine learning. Morgan Kaufmann, USA

32.

Craven M (1996) Extracting comprehensible models from trained neural networks. Ph.D. Dissertation, University of Wisconsin, Madison, Wisconsin

33.

Schmitz G, Aldrich C, Gouws F (1999) ANN-DT: an algorithm for extraction of decision trees from artificial neural networks. IEEE Trans Neural Netw 10(6):1392–1401. doi:10.1109/72.809084 CrossRef

34.

Timofeev R (2004) Classification and Regression Trees (CART) Theory and Applications. Masters Thesis, Humboldt University, Center of Applied Statistics and Economics, Berlin

35.

Quinlan J (1986) Induction of decision trees. In: Mitchell T (ed) Mach Learn, vol 1. McGraw-Hill, New York, pp 81–106

36.

Ziarko W, Yao Y (2001) A comparison of several approaches to missing values in data mining in Rough Sets and current trends in computing. Springer, Berlin

37.

Rangwala M (2006) Empirical investigation of decision tree extraction from neural networks. Ohio University, Department of Industrial and Systems Engineering, Athens, OH, USA

38.

Zhou ZH, Jiang Y, Chen SF (2003) Extracting symbolic rules from trained neural network ensembles. AI Commun 16(1):3–15

39.

Nath R, Rajagopalan B, Ryker R (1997) Determining the saliency of input variables in neural network classifiers. Comput Oper Res 24(8):767–773. doi:10.1016/S0305-0548(96)00088-3 MATHCrossRef

40.

Thrun S (1995) Extracting rules from artificial neural networks with distributed representations. In: Tesauro G, Touretzky D, Leen T (eds) Advances in Neural Information Processing Systems 7. MIT Press, Cambridge

41.

Lasdon L, Waren D, Jain A, Ratner M (1978) Design and testing of a generalized reduced gradient code for nonlinear programing. ACM Trans Math Softw 4(1):34–49. doi:10.1145/355769.355773 MATHCrossRef

42.

Watson J, Fylstra D (1996) Solver user’s guide. Frontline Systems, Incline Village

43.

Windward Technolgies and Optimal Methods (1997) User’s guide for GRG2 optimization library. Retrieved April 7, 2007, from http://www.maxthis.com/Grg2ug.htm

44.

Fylstra D, Lasdon L, Watson J (1998) Design and use of the microsoft excel solver. Interfaces 28(5):29–55. doi:10.1287/inte.28.5.29 CrossRef

45.

Lasdon L, Smith S (1992) Solving largesparse nonlinear programs using GRG. ORSA J Comput 1:2–15MathSciNet

46.

Newman D, Hettich S, Blake C, Merz C (1998) UCI repository of machine learning databases. (I. University of California, Producer, and Department of Information and Computer Sciences) Retrieved April 5, 2007, from ftp://ftp.ics.uci.edu/pub/machine-learning-databases/auto-mpg/

47.

NeuroDimension (2006) Intelligent Software Solutions. Retrieved 2007, from http://www.nd.com/

48.

Gill P, Murray W, Wright M (1981) Practical optimization. Academic Press, LondonMATH

Titel: Using a heuristic approach to derive a grey-box model through an artificial neural network knowledge extraction technique
verfasst von: William A. Young II
Gary R. Weckman
Publikationsdatum: 01.04.2010
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0270-2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 3/2010

Using near-infrared spectroscopy in the classification of white and iberian pork with neural networks

Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system

A comparison of binless spike train measures

Visualization and clustering of categorical data with probabilistic self-organizing map

Advanced machine learning techniques for microarray spot quality classification

Two-dimensional canonical correlation analysis and its application in small sample size face recognition

Premium Partner