Response Surface Modeling for Design Space Exploration of Embedded Systems - Multi-objective Design Space Exploration of Multiprocessor SoC Architectures - page 92

2. Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the marquardt algorithm.
IEEE Trans. on Neural Networks 5 (6) (1994)
3. Irie, B., Miyake, S.: Capabilities of three-layered perceptrons. In: Proceedings of the IEEE
International Conference on Neural Networks, pp. I-641 (1998)
4. Joseph, P., Vaswani, K., Thazhuthaveetil, M.: Construction and use of linear regression models
for processor performance analysis. High-Performance Computer Architecture, 2006. The
Twelfth International Symposium on pp. 99- 108 (2006)
5. Keutzer, K., Malik, S., Newton, A.R., Rabaey, J., Sangiovanni-Vincentelli, A.: System level
design: Orthogonolization of concerns and platform-based design. IEEE Transactions on
Computer-Aided Design of Integrated Circuits and Systems 19 (12), 1523-1543 (2000)
6. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural
Selection. MIT Press (1992)
7. Lee, B.C., Brooks, D.M.: Accurate and efficient regression modeling for microarchitectural
performance and power prediction. Proceedings of the 12th international conference on Ar-
chitectural support for programming languages and operating systems 40 (5), 185-194 (2006).
DOI http://doi.acm.org/10.1145/1168917.1168881
8. Matheron, G.: Les variables régionalisées et leur estimation: une application de la théorie des
fonctions aléatoires aux sciences de la nature. Masson, Paris (1965)
9. Montgomery, D.C., Runger, G.C.: Applied Statistics and Probability for Engineers.

Wiley

(2006)
10. Montgomery, D.C.: Design and Analysis of Experiments. John Wiley and Sons (2005)
11. Nguyen, D., Widrow, B.: Improving the learning speed of 2-layer neural networks by choosing
initial values of the adaptive weights. In: Proceedings of IJCNN, vol. 3, pp. 21-96 (1990)
12. Perelman, E., Hamerly, G., Biesbrouck, M.V., Sherwood, T., Calder, B.: Using simpoint for
accurate and efficient simulation. In: ACM SIGMETRICS Performance Evaluation Review,
pp. 318-319 (2003)
13. Powell, M.J.D.: The theory of radial basis functions. In: Advances in Numerical Analysis II:
Wavelets, Subdivision, and Radial Basis Functions, W. Light (ed, pp. 105-210. University
Press (1992)
14. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press

(2006)
15. Renau, J., Fraguela, B., Tuck, J., Liu, W., Prvulovic, M., Ceze, L., Sarangi, S., Sack, P.,
Strauss, K., Montesinos, P.: SESC simulator (2005). Http://sesc.sourceforge.net
16. Rigoni, E., Lovison, A.: Automatic sizing of neural networks for function approximation. In:
SMC2007, pp. 2005-2010 (2007)
17. Santner, T.J., B., W., W., N.: The Design and Analysis of Computer Experiments. Springer-
Verlag (2003)
18. Woo, S., Ohara, M., Torrie, E., Singh, J., Gupta, A.: Splash-2 programs: characterization and
methodological considerations. Proceedings of the 22th International Symposium on Computer
Architecture p. 2436 (1995)