By Robert H. Eisenman, Michael Wise
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Yn } . The error-correction algorithm is the basis of the learning phase, while the feature extraction mechanism is the featuring element of the models. In the following some details of the selected methods will be reported. 1 Object extraction network The system reported in [27,28] is two layered (without input layer) and each layer of dimension N X M is characterized by the following considerations • each neuron in a layer is connected to the corresponding of the previous layer and its neighbours.
1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics 43, 59-69. Kohonen, T. (1995). Self-Organizing Maps. Berlin: Springer-Verlag. Krzanowski, W. J. and F. H. C. Marriott (1994). Multivariate Analysis Part I: Distributions, Ordination and Inference. London: Edward Arnold. Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. The London, Edinburgh and Dublin Philosophical Magazine and Journal of Science, Sixth Series 2, 559-572.
We expect that, in the trained model, different regions of the latent space will correspond to different flight regimes. The first group of transition probabilities then allows 30 evolution of the data vector within a given flight regime to be modelled, while transitions to distant flight regimes can be represented by the second group. Note that this does not capture all of the likely behaviour of the time series. For instance, if transitions between two particular regimes are much more frequent than between some other pair of regimes, this cannot be represented in the model just described.