Regularized Length Changeable Extreme Learning Machine with Incremental Learning Enhancements for Remaining Useful Life Prediction of Aircraft Engines

The main objective of this works is to study and improve the performances of the Single hidden Layer Feedforward Neural network (SLFN) for the application of Remaining Useful Life (RUL) prediction of aircraft engines. The most common problems in SLFNs based old training algorithms such as backpropagation are time consuming, over-fitting and the appropriate network architecture identification. In t...