Coding and robustness of signal processing in streaming recurrent neural networks

Abstract

Introduction: When substantiating promising architectures of streaming recurrent neural networks, it becomes necessary to assess their stability in processing various input signals. For this, stability diagrams are constructed containing the results of simulation for each of the nodes of these diagrams. Such an estimation can be time-consuming and computationally intensive, especially when analyzing large neural networks. Purpose: Search for methods of quick construction of such diagrams and assessing the stability of streaming recurrent neural networks. Results: Analysis of the features of the stability diagrams under study showed that the nodes of the diagrams are grouped into continuous zones with the same ratio characteristics of the input signal processing defects. With this in mind, the article proposes a method for constructing these diagrams based on bypassing the boundaries of their zones. With this approach, you do not have to perform simulation for the interior nodes of each zone. The simulation should be performed only for the nodes adjacent to zone boundaries. Due to this, the number of nodes for which you need to perform simulation sessions is reduced by an order of magnitude. The influence of the input signal coding types on the streaming recurrent neural network stability has been investigated. It is shown that the representation of input signals in the form of sequences of single pulses with intersecting elements can provide greater stability as compared to pulses without any intersection.