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  • 1
    Electronic Resource
    Electronic Resource
    Neural network principal components preprocessing and diffraction tomography (1993)
    Springer
    Neural computing & applications 1 (1993), S. 107-114 
    Details
    ISSN: 1433-3058
    Keywords: Neural networks ; Principal component analysis ; Diffraction tomography ; Preprocessing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01414430
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  • 2
    Electronic Resource
    Electronic Resource
    Reducing error in neural network time series forecasting (1993)
    Springer
    Neural computing & applications 1 (1993), S. 240-247 
    Details
    ISSN: 1433-3058
    Keywords: Neural network ; Time series forecasting ; Backpropagation ; Conjugate gradients ; Autocorrelation error ; Post-processing ; Sunspot series
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Neural network time series forecasting error comprises autocorrelation error, due to an imperfect model, and random noise, inherent in the data. Both problems are addressed here, the first using a two stage training, growth-network neuron: the autocorrelation error (ACE) neuron. The second is considered as a post-processing noise filtering problem. These techniques are applied in forecasting the sunspot time series, with comparison of stochastic, BFGS and conjugate gradient solvers.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02098741
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    Paper (German National Licenses)
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