Using artificial neural networks for process and system modelling

Chemometrics and Intelligent Laboratory Systems

Volumn 67, Issue 2, 2003, Pages 187-191

  Verikas, A ^a,b   Bacauskiene, M ^b  

^a HALMSTAD UNIVERSITY   (Sweden)

^b KAUNAS UNIVERSITY OF TECHNOLOGY   (Lithuania)

Author keywords

Initialization; Neural network; Over fitting; Regularization

Indexed keywords

ALGORITHM; ANALYTICAL ERROR; ARTIFICIAL NEURAL NETWORK; CHEMOMETRICS; COMPUTER AIDED DESIGN; COMPUTER MODEL; EVALUATION; INFORMATION PROCESSING; LABORATORY AUTOMATION; LETTER; MATHEMATICAL COMPUTING; PARAMETER; PRIORITY JOURNAL;

EID: 0042031251     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0169-7439(03)00093-5     Document Type: Letter

References (16)

1. S. Lawrence, C.L. Giles, A.C. Tsoi, What size neural network gives optimal generalization? Convergence properties of backpropagation, Tech. Rep. UMIACS-TR-96-22, Institute for Advanced Computer Studies, University of Maryland, 1996.
2. Bartlett P.L. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans. Inf. Theory. 44(2):1998;525-536.
3. Haykin S. Neural Networks. A Comprehensive Foundation. 2nd ed. 1999;Prentice-Hall, Upper Saddle River, New Jersey.
4. Lee W.S., Bartlett P.L., Williamson R.C. Efficient agnostic learning of neural networks with bounded fan-in. IEEE Trans. Inf. Theory. 42(6):1996;2118-2132.
5. Atiya A., Ji C. How initial conditions affect generalization performance in large networks. IEEE Trans. Neural Netw. 8(2):1997;448-451.
6. Y. LeCun, Efficient Learning and Second-order Methods, A tutorial at NIPS 93, Denver, 1993.
7. Amari S., Murata N., Muller K.R., Finke M., Yang H. Statistical theory of overtraining - is cross-validation asymptotically effective? Touretzky S., Mozer M.C., Hasselmo M. Advances in Neural Information Processing Systems. vol. 8:1996;176-182 MIT Press, Massachusetts.
8. MacKay D.J. Bayesian interpolation. Neural Comput. 4:1992;415-447.
9. Foresee F.D., Hagan M.T. Gauss-Newton approximation to Bayesian learning. Proc. IEEE Int. Joint Conf. Neural Netw. 1997;1930-1935.
10. Edwards P.J., Murray A.F. Toward optimally distributed computation. Neural Comput. 10:1998;997-1015.
11. Bishop C.M. Neural Networks for Pattern Recognition. 1995;Clarendon Press, Oxford.
12. Verikas A., Lipnickas A., Bacauskiene M., Malmqvist K. Fusing neural networks through fuzzy integration. Bunke H., Kandel A. Hybrid Methods in Pattern Recognition. 2002;227-252 World Scientific, Singapore.
13. Verikas A., Lipnickas A., Malmqvist K., Bacauskiene M., Gelzinis A. Soft combination of neural classifiers: a comparative study. Pattern Recogn. Lett. 20:1999;429-444.
14. Hastie T., Tibshirani R., Friedman J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics). 2001;Springer-Verlag, New York.
15. Efron B. The convex hull of a random set of points. Biometrika. 52:1979;331-342.
16. Efron B., Tibshirani R. An Introduction to the Bootstrap. 1993;Chapman & Hall, London.