ProMoS NG - Building Intelligence
Over 30 different algorithms are implemented in ProMoS NG. The system evaluates the suitable algorithms completely automatically.
Functions
Usable functions
The BI module of ProMoS NG opens up a wide range of possible applications for machine learning:
- Create tasks: Users can define explanatory variables (e.g. process data points), temporal resolutions and time spans. A variable limit value is calculated on this basis, which depends on both the specified variables and the temporal position of the values. The ProMoS-BI module independently evaluates the optimal models to ensure the best possible results.
- Another option is a specially set up test environment: the user can make all the settings here independently, select suitable ML models and have the calculations carried out.
- Productive evaluations: The calculations are not just carried out once, but in regular cycles. This enables continuous monitoring of the variables.
Models
Machine learning models
The following ML algorithms are currently implemented:
- AdaBoostRegressor
- BaggingRegressor
- DecisionTreeRegressor
- ExtraTreesRegressor
- GammaRegressor
- GaussianPricessRegressor
- GradientBoostingRegressor
- HistGradientBoostingRegressor
- HuberRegressor
- KNeighborsRegressor
- KernelRigde
- Lars
- LarsCV
- LassoCV
- LassoLarsCV
- LassoLarcIV
- LinearRegression
- LinearSVR
- MLPRegressor
- NUSVR
- OrthoganaMatchingPursuit
- OrthoganaMatchingPursuitCV
- PiecewiseRegressor
- RANSACRegressor
- RadiusNeighborsRegressor
- RandomForestRegressor
- RidgeCV
- SVR
- TheilSenRegressor
- XGBoos