
References 327
to Predict the Wear Behaviour of Com-
posite Materials.’’ Proceedings der 9th
European Conference on Composite
Materials (ECCM 9) Brighton, UK, er-
schienen auf CD-ROM, June 2000.
58 Velten, K., Reinicke, R. and Friedrich, K.
(2000) ‘‘Neuronale Netze zur Vorhersage
und Analyse des Verschleissverhaltens
polymerer Gleitwerkstoffe’’. Materialwis-
senschaft und Werkstofftechnik, 31, 715–18.
59 Zhang, Z., Friedrich, K. and Velten,
K. (2002) ‘‘Prediction of tribologi-
cal properties of short fibre com-
posites using artificial neural net-
works’’. Wear, 252(7-8), 668–75.
60 Zhang, Z., Reinicke, R., Klein, P.,
Friedrich, K. and Velten, K. (2001)
Wear Predicition of Polymeric Com-
posites Using Artificial Neural Net-
works. Proceedings of the International
Conference on Composites in Mate-
rial and Structural Engineering, Vol. 6,
Prague, Czech Republic, pp. 203–6.
61 Cybenko, G. (1989) ‘‘Approximation
by superpositions of a sigmoidal func-
tion’’. Mathematics of Control, Sig-
nals, and Systems, 2(4), 303–14.
62 Funahashi, K. (1989) ‘‘On the ap-
proximate realization of continu-
ous mappings by neural networks’’.
Neural Networks, 2(3), 183–92.
63 Hornik, K., Stinchcombe, M. and
White, H. (1989) ‘‘Multilayer feedfor-
ward networks are universal approxima-
tors’’. Neural Networks, 2(5), 359–66.
64 Ripley, B.D. (1996) Pattern Recognition and
Neural Networks, Cambridge University
Press.
65 Binmore, K. and Davies, J. (2002) Calcu-
lus: Concepts and Methods, Cambridge Uni-
versity Press.
66 Mitchell, T.M. (1997) Machine Learning,
Mc Graw-Hill.
67 Ripley, B.D. (1993) Statistical aspects
of neural networks, Networks and
Chaos - Statistical and Probabilistic As-
pects, Chapman & Hall, pp. 40–123.
68 Ripley, B.D. (1994) Neural networks and
flexible regression and discrimination, Ad-
vances in Applied Statistics,Vol.2,Carfax,
pp. 39–57.
69 Bear, J. (1990) Introduction to Modeling
of Transport Phenomena in Porous Media,
Kluwer Academic Publishers, Dordrecht,
Netherlands.
70 Lasaga, A. (1998) Kinetic Theory in the
Earth Sciences, Princeton University Press.
71 Abebe, A., Daniels, J. and McKean,
J.W. (2001) Statistics and Data Analy-
sis. Technical report. Statistical Com-
putation Lab (SCL), Western Michi-
gan University, Kalamazoo, MI.
72 Montgomery, D.C. (2005) Design and Anal-
ysis Experiments, John Wiley & Sons, Ltd.
73 Soravia, S. and Orth, A. (2007)Design
of Experiments, Ullmann’s Modeling and
Simulation, John Wiley & Sons, Ltd, pp.
363–400.
74 Inuiguchi, M., Tsumoto, S. and Hirano,
S. (eds) (2007) Rough Set Theory and
Granular Computing, Springer.
75 Tettamanzi, A. and Tomassini, M. (2001)
Soft Computing: Integrating Evolutionary,
Neural, and Fuzzy Systems, Springer.
76 Falkenauer, E. (1997) Genetic Algorithms
and Grouping Problems, John Wiley &
Sons, Ltd.
77 Hill, T., Lundgren, A., Fredriksson, R. and
Schi
¨
oth, H.B. (2005) ‘‘Genetic algorithm
for large-scale maximum parsimony phy-
logenetic analysis of proteins’’. Biochim-
ica et Biophysica Acta, 1725, 19–29.
78 Kjellstr
¨
om, G. (1996) ‘‘Evolution as
a statistical optimization algorithm’’.
Evolutionary Theory, 11, 105–17.
79 Eberhart, R.C., Shi, Y. and Kennedy, J.
(2001) Swarm Intelligence, Morgan Kauf-
mann.
80 Zadeh, L.A. (1965) ‘‘Fuzzy sets’’. In-
formation and Control, 8, 338–53.
81 Ibrahim, A. (2003) Fuzzy Logic for Em-
bedded Systems Applications,Newnes.
82 Marshall, C. and Rossman, G.B. (2006)
Designing Qualitative Research, Sage Publi-
cations.
83 Scholz,R.W.andTietje,O.(2002)
Embedded Case Study Methods: In-
tegrating Quantitative and Qualita-
tive Knowledge, Sage Publications.
84 Yin, R.K. (2003) Case Study Research, De-
sign and Methods, Sage Publications.
85 Rubinstein, R.Y. and Melamed, B. (1998)
Modern Simulation and Modeling.
86 Bratley,P.,Fox,B.L.andSchrage,L.E.
(1987) A Guide to Simulation, Springer.
87 Petzoldt,T.andRinke,K.(2007) ‘‘An
object-oriented framework for ecological