Abstract
The features of recurrent neural networks with controlled elements are considered. The functions of these networks for controlled associative processing of signals are refined. A number of the space-time structures of such networks are analyzed. Among them, the neural networks with one-, two-, and three-level structures of layers are investigated. The results of studies on the stability of the associative processing of distorted signals by these networks are reflected. Based on the simulation results, recommendations are formulated to expand the possibilities of associative signal processing in recurrent neural networks with controlled elements.
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Acknowledgement
This research is partially supported by the RFBR foundation grant No 16-29-09482.
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Osipov, V., Nikiforov, V. (2019). Functional and Structural Features of Recurrent Neural Networks with Controlled Elements. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_15
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DOI: https://doi.org/10.1007/978-3-030-22796-8_15
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