Abstract
In this paper, a method for increasing the number of multilayer perceptron inputs has been proposed. Three kinds of additional input variables have been tested. They make it possible to perform data separation by neurons in the first layer of multilayer perceptrons, with the use of hypercurves having various shapes in the two selected dimensions. By using more inputs, single neurons in the first hidden layer are capable of solving some non-linear separable problems, e.g. the XOR function. In dependence of the weight values of these neurons, they may, in some dimensions, realise the similar transformations as neurons in the hidden layer of RBF networks or separated the data with hyperplanes or hyperparabolas. The use of the proposed procedure does not need to implement, from the very beginning, a new network training algorithm. The classification results on the three very popular UCI benchmarks, which contain the real-world data, are presented.
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Halawa, K. (2016). Method Enabling the First Hidden Layer of Multilayer Perceptrons to Make Division of Space with Various Hypercurves. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9692. Springer, Cham. https://doi.org/10.1007/978-3-319-39378-0_10
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DOI: https://doi.org/10.1007/978-3-319-39378-0_10
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