Title: Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration,
Abstract: This article expands on research that has been done to develop a recurrent
neural network (RNN) capable of predicting aircraft engine vibrations using
long short-term memory (LSTM) neurons. LSTM RNNs can provide a more
generalizable and robust method for prediction over analytical calculations of
engine vibration, as analytical calculations must be solved iteratively based
on specific empirical engine parameters, making this approach ungeneralizable
across multiple engines. In initial work, multiple LSTM RNN architectures were
proposed, evaluated and compared. This research improves the performance of the
most effective LSTM network design proposed in the previous work by using a
promising neuroevolution method based on ant colony optimization (ACO) to
develop and enhance the LSTM cell structure of the network. A parallelized
version of the ACO neuroevolution algorithm has been developed and the evolved
LSTM RNNs were compared to the previously used fixed topology. The evolved
networks were trained on a large database of flight data records obtained from
an airline containing flights that suffered from excessive vibration. Results
were obtained using MPI (Message Passing Interface) on a high performance
computing (HPC) cluster, evolving 1000 different LSTM cell structures using 168
cores over 4 days. The new evolved LSTM cells showed an improvement of 1.35%,
reducing prediction error from 5.51% to 4.17% when predicting excessive engine
vibrations 10 seconds in the future, while at the same time dramatically
reducing the number of weights from 21,170 to 11,810.