Name
Abstract | ||
|---|---|---|
This work presents the use of an ant colony optimization (ACO) based neuro-evolution algorithm to optimize the structure of a long short-term memory (LSTM) recurrent neural network (RNN) for the prediction of aircraft turbine engine vibrations. It expands upon previous work using three different LSTM architectures, with the new evolved LSTM cells showing an improvement of 1.35%, reducing prediction error from 5.51% to 4.17% when predicting excessive engine vibrations 10 seconds in the future. These results were gained using MPI on a high performance computing cluster, evolving 1000 different LSTM cell structures using 168 cores over 4 days. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3067695.3082045 | GECCO (Companion) |
Keywords | Field | DocType |
Ant Colony Optimization, ACO, Long Short Term Memory Recurrent Neural Network, LSTM, RNN, Aviation, Turbine engine vibration | Ant colony optimization algorithms,Mean squared prediction error,Supercomputer,Computer science,Recurrent neural network,Artificial intelligence,Turbine,Vibration,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.36 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| AbdElRahman ElSaid | 1 | 2 | 1.37 |
| Brandon Wild | 2 | 15 | 2.60 |
| Fatima El Jamiy | 3 | 7 | 2.18 |
| James Higgins | 4 | 15 | 2.94 |
| Travis Desell | 5 | 116 | 18.56 |