Use of Parallel Explanatory Models to Enhance Transparency of Neural
  Network Configurations for Cell Degradation Detection
David Mulvey Chuan Heng Foh Muhammad Ali Imran Rahim Tafazolli
In a previous paper, we have shown that a recurrent neural network (RNN) can
be used to detect cellular network radio signal degradations accurately. We
unexpectedly found, though, that accuracy gains diminished as we added layers
to the RNN. To investigate this, in this paper, we build a parallel model to
illuminate and understand the internal operation of neural networks, such as
the RNN, which store their internal state in order to process sequential
inputs. This model is widely applicable in that it can be used with any input
domain where the inputs can be represented by a Gaussian mixture. By looking at
the RNN processing from a probability density function perspective, we are able
to show how each layer of the RNN transforms the input distributions to
increase detection accuracy. At the same time we also discover a side effect
acting to limit the improvement in accuracy. To demonstrate the fidelity of the
model we validate it against each stage of RNN processing as well as the output
predictions. As a result, we have been able to explain the reasons for the RNN
performance limits with useful insights for future designs for RNNs and similar
types of neural network.