Uniform design applied to nonlinear multivariate calibration by ANN

[1]  W. J. Studden,et al.  Theory Of Optimal Experiments , 1972 .

[2]  H. Niederreiter.  Quasi-Monte Carlo methods and pseudo-random numbers , 1978 .

[3]  George E. P. Box,et al.  Empirical Model‐Building and Response Surfaces , 1988 .

[4]  J. E. Reece,et al.  Experimental Design: A Chemometric Approach , 1987 .

[5]  Harald Niederreiter,et al.  Quasi-Monte Carlo Methods for Multidimensional Numerical Integration , 1988 .

[6]  Kombination von optimaler Versuchsplanung und einigen Matrixmethoden zur Mehrkomponentenkalibrierung und — analyse , 1988 .

[7]  D. Wiens.  Designs for approximately linear regression: two optimality properties of uniform designs , 1991 .

[8]  Paul J. Gemperline,et al.  Nonlinear multivariate calibration using principal components regression and artificial neural networks , 1991 .

[9]  M. Bos,et al.  Comparison of the training of neural networks for quantitative x-ray fluorescence spectrometry by a genetic algorithm and backward error propagation , 1991 .

[10]  Harald Niederreiter,et al.  Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.

[11]  A. Bos,et al.  Artificial neural networks as a tool for soft-modelling in quantitative analytical chemistry: the prediction of the water content of cheese , 1992 .

[12]  H. H. Thodberg,et al.  Optimal minimal neural interpretation of spectra , 1992 .

[13]  K. Fang,et al.  Number-theoretic methods in statistics , 1993 .

[14]  Johann Gasteiger,et al.  A combined application of two different neural network types for the prediction of chemical reactivity , 1993 .

[15]  Incorporation of Plant Plasmalemma Into Planar Lipid Bilayer and Function of Transmembrane Transport , 1994 .

[16]  Nineta Majcen,et al.  Modeling of property prediction from multicomponent analytical data using different neural networks , 1995 .

[17]  P. Laycock,et al.  Optimum Experimental Designs , 1995 .

[18]  Desire L. Massart,et al.  Artificial neural networks in classification of NIR spectral data: Design of the training set , 1996 .