Robust polynomial neural networks in quantative-structure activity relationship studies

Systems Analysis Modelling Simulation

Volumn 43, Issue 10, 2003, Pages 1331-1339

  Aksyonova, Tetyana I ^a   Volkovich, Vladimir V ^b   Tetko, Igor V ^c  

^a NATIONAL TECHNICAL UNIVERSITY OF UKRAINE   (Ukraine)

^b International Researching Training Center of Information Technologies   (Ukraine)

^c UNIVERSITY OF LAUSANNE   (Switzerland)

Author keywords

Chemical engineering; Forecasting; GMDH type neural network; Nonlinear regression analysis; Robust estimation

Indexed keywords

ALGORITHMS; FORECASTING; ITERATIVE METHODS; MATHEMATICAL MODELS; POLYNOMIALS; REGRESSION ANALYSIS;

GMDH-TYPE NEURAL NETWORK; NONLINEAR REGRESSION ANALYSIS; ROBUST ESTIMATION; ROBUST POLYNOMIAL NEURAL NETWORKS;

NEURAL NETWORKS;

EID: 13444260431     PISSN: 02329298     EISSN: 10294902     Source Type: Journal    
DOI: 10.1080/02329290290024330     Document Type: Conference Paper

References (19)

1. I.V. Tetko, V.Y. Tanchuk and A.I. Luik (1994). Application of an evolutionary algorithm to the structure-activity relationship. In: Proceedings 3rd Annual Conference on Evolutionary Programming. pp. 109-119. World Scientific, River Edge, N.J.
2. H. Kubinyi (1996). Evolutinary variable selection in regression and PLS analyses. Journal of Chemometrics, 10, 119-133.
3. S. Rannar, F. Lindgren, P. Geladi and S. Wold (1994). A PLS kernel algorithm for data sets with many variables and fewer objects. 1. Theory and algorithm. Journal of Chemometrics, 8, 111-125.
4. J. Zupan and J. Gasteiger (1999). Neural Networks for Chemistry and Drug Design: An Introduction; 2nd edn. VCH, Weinheim.
5. I.V. Tetko, D.J. Livingstone and A.I. Luik (1995). Neural network studies. 1. comparison of overfitting and overtraining. Journal of Chemical Information and Computer Sciences, 35, 826-833.
6. H.R. Madala, A.G. Ivakhneko (1994). Inductive Learning Algorithms for Complex Systems Modelling, CRC Press Inc., Boca Raton.
7. T.I. Aksenova and Y.P. Yurachkovsky (1988). Characterization of unbiased structure and condition of its J-optimality. Soviet Journal of Automation and Information Science, 21, 24-32.
8. Y. P. Yurachkovsky (1981). Restoration of polynomial dependencies using self-organization. Soviet Automatic Control, 14, 17-22.
9. D. Rothman (1968). Letter to the editor. Technometrics, 10, 661-667.
10. H. Akaike (1969). Fitting autoregressive models for prediction. Ann. Inst. Statist. Math., 21, 243-247.
11. H. Akaike (1974). A new look at the statistical model identification. IEEE Trans. Automatic Control, AC-19, 716-723.
12. G. Schwarz (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461-464.
13. A.A. Neath and J.E. Cavanaugh (1997). Regression and time series model selection using variants of the Schwarz information criterion. Communications in Statistics - Theory and Methods, 26, 559-580.
14. Y.P. Yurachkovsky (1981). Convergence of multilayer algorithms of the group method of data handling. Soviet Automatic Control, 14, 29-35.
15. A.G. Ivakhncnko and Y.P. Yurachkovsky (1987). Complex system modeling on experimental data. Radio and Communication. Moscow. (in Russian)
16. I.V. Tetko, T.I. Aksenova, V.V. Volkovich, T.N. Kasheva, D.V. Filipov, W.J. Welesh, D.J. Livingstone and A.E.P. Villa (2000). Polynomial neural network for linear and non-linear model selection in quantitative-structure activity relationship studies on the Internet. SAR QSAR Environ. Res., 11, 263-280
17. P.J. Huber (1981). Robust Statistics. Wiley and Sons Inc., N.-Y.
18. F.R. Hampel, E.M. Ronchetti, P.J. Rousseeuw, W.A. Stahel (1989). Robust Statistics. The approach based on influence functions. Wiley and Sons Inc., N.-Y.
19. W.H. Press, S.A. Teukolsky, W.T., Vetterling and B.P. Flannery (1994). Numerical Recipes in C, 2nd edn. Cambridge University Press, New York.