Abstract
This paper describes the issue of error level fluctuations due to training set shrinking in RBF-networks. An architecture of artificial neural network (ANN) based on RBF-network is presented with a learning algorithm to train it. The presented architecture is multi-layer, unlike original RBF-network thus has a potential in deep learning. Numeric results lead to a conclusion about error level fluctuations being significantly lower for the presented architecture compared to RBF-network in case of training set shrinking. This displays a greater generalization ability of the presented architecture. The paper contains an application of ANN to the task of restoring the dielectric parameters for subject placed in waveguide.
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Abrosimov, M., Brovko, A. (2019). High Generalization Capability Artificial Neural Network Architecture Based on RBF-Network. In: Dolinina, O., Brovko, A., Pechenkin, V., Lvov, A., Zhmud, V., Kreinovich, V. (eds) Recent Research in Control Engineering and Decision Making. ICIT 2019. Studies in Systems, Decision and Control, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-030-12072-6_7
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DOI: https://doi.org/10.1007/978-3-030-12072-6_7
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