Log In Sign Up

Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach

by   Sayan Saha, et al.

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.


Deep Learning: Extrapolation Tool for Ab Initio Nuclear Theory

Ab initio approaches in nuclear theory, such as the No-Core Shell Model ...

A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output LC Filter

Model predictive control (MPC) has become one of the well-established mo...

Artificial Neurons with Arbitrarily Complex Internal Structures

Artificial neurons with arbitrarily complex internal structure are intro...

Lorenz System State Stability Identification using Neural Networks

Nonlinear dynamical systems such as Lorenz63 equations are known to be c...