Development of a generalized neural network

Chemometrics and Intelligent Laboratory Systems

Volumn 50, Issue 1, 2000, Pages 101-105

  Andersson, Greger G ^a,b   Kaufmann, Peter ^b,c  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

^b Karlshamns LipidTeknik AB   (Sweden)

^c STOCKHOLM UNIVERSITY   (Sweden)

Author keywords

Algorithms; Calibration set; Neural networks

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALIBRATION; CHEMOMETRICS; NONLINEAR SYSTEM; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

EID: 0033964996     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0169-7439(99)00051-9     Document Type: Article

References (19)

1. Zupan J., Gasteiger J. Neural networks: a new method for solving chemical problems or just a passing phase. Anal. Chim. Acta. 248:1991;1-30.
2. Kateman G. Neural networks in analytical chemistry? Chemom. Intell. Lab. Syst. 19:1993;135-142.
3. J. Zupan, J. Gasteiger, Neural Networks for Chemists: An Introduction, VCH, Weinheim, Germany, 1993, pp. 119-148.
4. Svozil D., Kvasnicka V., Pospichal J. Introduction to multi-layer feed-forward neural networks. Chemom. Intell. Lab. Syst. 39:1997;43-62.
5. Leonard J., Kramer M.A. Improvement of the backpropagation algorithm for training neural networks. Comput. Chem. Eng. 14:1990;337-341.
6. Blank T.B., Brown S.D. Adaptive, global, extended Kalman filters for training feedforward neural networks. J. Chemom. 8:1994;391-407.
7. Harrington P.B. Temperature-constrained backpropagation neural networks. Anal. Chem. 66:1994;802-807.
8. Klawun C., Wilkins C.L. A novel algorithm for local minimum escape in back-propagation neural networks: application to the interpretation of matrix isolation infrared spectra. J. Chem. Inf. Comput. Sci. 34:1994;984-993.
9. F. Stäger, M. Agarwal, Improved training of neural networks with complex search spaces, IFAC Advanced Control of Chemical Processes, Kyoto, Japan, 1994, pp. 65-70.
10. Robitaille R., Marcos B., Veillette M., Payre G. Modified quasi-Newton methods for training neural networks. Comput. Chem. Eng. 20:1996;1133-1140.
11. Walczak B. Neural networks with robust backpropagation learning algorithm. Anal. Chim. Acta. 322:1996;21-29.
12. H. Demuth, M. Beale, Neural Network Toolbox User's Guide, Chap. 13, The MathWorks, Natick, MA, 1994, pp. 135-137.
13. Kateman G. Evolution in chemometrics. Analyst. 115:1990;487-493.
14. Lucasius C.B., Kateman G. Genetic algorithms for large-scale optimization in chemometrics: an application. Trends Anal. Chem. 247:1991;254-261.
15. Bos M., Weber H.T. Comparison of the training of neural networks for quantitative X-ray fluorescence spectrometry by a genetic algorithm and backward error propagation. Anal. Chim. Acta. 247:1991;97-105.
16. Jiang J.-H., Wang J.-H., Song X.-H., Yu R.Q. Network training and architecture optimization be a recursive approach and a modified genetic algorithm. J. Chemom. 10:1996;253-267.
17. Sánches M.S., Sarabia L.A. GINN (Genetic Inside Neural Network): towards a non-parametrics training. Anal. Chim. Acta. 348:1997;533-542.
18. Ajay On better generalization by combining two or more models: a quantitative structure-activity relationship example using neural networks. Chemom. Intell. Lab. Syst. 24:1994;19-30.
19. H. Demuth, M. Beale, Neural Network Toolbox User's Guide, Chap. 13, The MathWorks, Natick, MA, 1994, p. 75.