Performance of multi layer feedforward and radial base function neural networks in classification and modelling

Chemometrics and Intelligent Laboratory Systems

Volumn 33, Issue 2, 1996, Pages 101-119

  Sanchez M S ^a   Swierenga, H ^b   Sarabia, L A ^a   Derks, E ^b   Buydens, L ^b  

^a UNIVERSITY OF BURGOS   (Spain)

^b RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

Author keywords

Classification; Modelling; Multi layer feedforward; Neural networks; Radial base function

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; SYSTEM ANALYSIS; THEORY;

EID: 0030174850     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/0169-7439(95)00089-5     Document Type: Article

References (28)

1. [1] E.P.P.A. Derks, M.S. Sánchez Pastor and L.M.C. Buydens, Chemom. Intelligent Lab. Sys., 28 (1995) 49-60.
2. [2] M.P. Derde, L. Kaufman and D.L. Massart, J. Chemom., 3 (1989) 375-395.
3. [3] M.P. Derde and D.L. Massart, Anal. Chim. Acta, 184 (1986) 33-51.
4. [4] M. Forina, S. Lanteri and L. Sarabia, J. Chemom., 9 (1995) 69-89.
5. [5] A. Bos, Artificial Neural Networks as a Tool in Chemometrics, Ph.D. Thesis, University of Twente, Enschede, The Netherlands, 1993.
6. [6] D.A. White and D.A. Sofge, Handbook of Intelligent Control, Van Nostrand Reinhold, New York, 1992.
7. [7] J. Zupan and J. Gasteiger, Networks for Chemists. An Introduction, VCH, Weinheim, 1993.
8. [8] M. Carlin, T. Kavli and B. Lillekjendlie, Chemom. Intelligent Lab. Sys., 23 (1994) 163-177.
9. [9] M. Hartnett, D. Diamond and P.C. Barker, Analyst, 118 (1993) 347-354.
10. [10] T.B. Blank and S.D. Brown, Anal. Chem., 65 (1993) 3081-3089.
11. [11] J. Park and I.W. Sandberg, Neural Comput., 5 (1993) 305-316.
12. [12] K.M. Hornik, M. Stinchcombe and H. White, Neural Networks, 2 (1989) 359-366.
13. [13] H. Lohninger, J. Chem. Inf. Comput. Sci., 33 (1993) 736-744.
14. [14] D.E. Rumelhart, G.E. Hinton and R.J. Williams, Learning internal representations by error propagation, in D.E. Rumelhart and J.L. McClelland (Eds.), Parallel Distributed Processing, Vol. 1, MIT Press, Cambridge, MA, 1986.
15. [15] N. Draper and H. Smith, Applied Regression Analysis, 2nd edn., New York, 1981.
16. [16] M.C. Ortiz, J.A. Sáez and J. López, Analyst, 118 (1993) 801-805.
17. [17] M.S. Sánchez and L.A. Sarabia, Chemom. Intelligent Lab. Sys., 28 (1995) 287-303.
18. [18] J.R.M. Smits, W.J. Melssen, L.M.C. Buydens and G. Kateman, Chemom. Intelligent Lab. Sys., 22 (1994) 165-189.
19. [19] L.-X. Sim et al., Chemom. Intelligent Lab. Sys., 25 (1994) 51-60.
20. [20] T. Kohonen, Self-Organization and Associative Memory, Springer, Berlin, 1989.
21. [21] D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York, 1989.
22. [22] H. Demuth and M. Beale, Neural Network Toolbox User's Guide, The MathWorks Inc., Natick, MA, 1993.
23. [23] M.I. Jordan and R.A. Jacobs, Neural Comput., 6 (1994)181-214.
24. [24] R. Henrion, Chemom. Intelligent Lab. Sys., 25 (1994) 1-23.
25. [25] L.R. Tucker, Psychometrika, 31 (1966) 279-311.
26. [26] T.P. Hettmansperger, Statistical Inference Based on Ranks, Wiley, NY, 1984.
27. [27] M. Forina, R. Leardi, C. Armanino and S. Lanteri, PARVUS: An extendable Package of Programs for Data Exploration, Classification and Correlation, Release 1.2 available from the authors, Genoa, Italy, 1994.
28. [28] J.A. Leonard, M.A. Kramer and L.H. Ungar, Comput. Chem. Eng., 16 (1992) 819-836.