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Verbal factors are preferred over mathematical formulation, graphs are saved to a minimal, and line drawings are utilized in this easy booklet. transparent tips and recommendation are supplied for these pros who lay out analog circuits.
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This is shown on the scatter diagram of observed (on the x axis) and calculated values (on the y axis) for each land use (figures 6 a, b and c). Diappi, Bolchi, and Buscema 45 The spatial representation of “errors” allows the evaluation of the spatial logic of SANNs output. Figure 7 shows the errors concerning the residential land use. Errors are measured in ratio over the whole cell surface. Table 2. Statistical measures of validation Residential Industrial RMSE Real Error Relative Error Error Variance NMSE Squared R Linear Corr.
The results are presented and discussed in this section. Starting with the parameters suggested by the package EasyNN-plus we have built networks with one or two hidden layers, with different number of nodes in each of the hidden layers, and with distinct learning rate (L) and momentum (M) values. The schemes shown in Figures 4 and 5 have the values of L and M grouped according to the distinct network topologies tested. In those Figures, the average and standard deviation of MRE values for the three groups of data randomly selected as training and validation subsets are also presented for each network tested, along with the results of the query data in the right-hand boxes.
1990, Neurocomputing, Addison-Wesley, Reading, MA. Jain, A. , J. Mao and K. M. Mohiuddin, 1996, Artificial neural networks: a tutorial, Comput. IEEE March, p. 31-44. , J. H. Ryu and M. J. Lee, 2003, “Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea”, Environmental Geology, Vol. 44, p. 820-833. Mayoraz, F. and L. Vulliet, 2002, “Neural networks for Slope Movement Prediction”, The International Journal of Geomechanics, Vol. 2, Num. 2, p. 153-173.