Prediction with Back-Propagation and Linear Regression
Prediction of the power of the turbine of a hydro-electrical plant, using the following algorithms:
- Back-Propagation (BP), implemented by the student
- Multiple Linear Regression (MLR), using free software
- File: turbine.txt
- Columns: 4 variables, 1 value t - predict
- Variables:
- Height above sea level
- Fall
- Net fall
- Flux
- Prediction:
- Power of the turbine
- Variables:
- Patterns: 451 patterns
- Training and Validation (and cross-validation): the first 401 patterns
- Test: the remaining 50 patterns
- Try different values of the training parameters and select those with the best
results:
- Architecture of the neural network
- Type of scaling of the data
- Initial range of weights and thresholds
- Learning rate and Momentum
- Batched/online
- Number of training epochs