The paper investigates nonlinear system identification using system output
data at various linearized operating points. A feed-forward multi-layer
Artificial Neural Network (ANN) based approach is used for this purpose and
tested for two target applications i.e. nuclear reactor power level monitoring
and an AC servo position control system. Various configurations of ANN using
different activation functions, number of hidden layers and neurons in each
layer are trained and tested to find out the best configuration. The training
is carried out multiple times to check for consistency and the mean and
standard deviation of the root mean square errors (RMSE) are reported for each
configuration.Comment: "6 pages, 9 figures; The Second IEEE International Conference on
Parallel, Distributed and Grid Computing (PDGC-2012), December 2012, Solan