Abstract
The use of principal component analysis in preprocessing neural network input data is explored. Four preprocessing schemes are compared in an example problem, and the theoretical basis for the results are discussed. A preconditioning method for the principal components is introduced here, combining normalisation and improved conditioning. The techniques are applied to an object location problem in diffraction tomography. The spectral analysed scattered field from an irradiated object form the input to a Multilayer Perceptron neural network, trained by backpropagation to calculate the coordinates of the object's centre in 2D.
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Azoff, E.M. Neural network principal components preprocessing and diffraction tomography. Neural Comput & Applic 1, 107–114 (1993). https://doi.org/10.1007/BF01414430
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DOI: https://doi.org/10.1007/BF01414430