Key Features

• Import Excel files

• Import CSV and TXT files

• Import bitmap image files

• Import binary files

• Extensive pre-processing facilities

• Date and Time encoding

• Integer, real, boolean, text or image modes

• Many methods of handling missing values

• Min/Max column values for scaling

• Outlier handling

• Random and manual data partitioning

• Data subsets

• Check for duplicate rows

• Column value classification

• Range extender and filler

• Shuffle rows

• Input and output selection

• Multiple inputs and outputs

• Check rows and columns are suitable to build a network

• Automatic or manual production of hidden layers

• Automatic or manual learning rate and momentum

• Automatic decay of learning rate and momentum

• Global or independent input and output validating rules

• Scoring rules

• Automatic or manual network reconfiguration while learning

• Stop after fixed number of cycles

• Node and Weight freezing

• Variable validating periods

• Fixed time stop

• Variable speed learning for visual demonstrations

• Many methods of early stopping

• Validating correct or within range

• Jitter and Noise

• Random and Balanced presentation

• Save any part of the network to TXT or CSV files

• Save the data grid to TXT or CSV files

• Save learning progress to TXT or CSV files

• Save backup while learning

• Variable save period

• Save when error reduces

• Save when validating results improve

• Record and Play macros

• Extensive script language

• Add commands and scripts to macros

• Run scripts from the command line or other applications

• Single step macros

• Run background scripts while hidden

• Query trained networks manually

• Query networks from external files

• See output values change when changing input values

• Seek high or low outputs

• Cycle seek though all inputs

• Save results to TXT or CSV files

• Query inputs can be extended beyond the training range

• Extrapolated results can be produced

• Forecast future values with multiple networks

• Allow forecasts to extend beyond training limits

• Assess risk of forecasts

• Restrict forecasts to upper or lower training limits

• Automatically find associated inputs and outputs

• Find inputs and outputs that form clusters

• Save associations and clusters

• Build networks from clusters

• Sequential leave out subsets selection

• Random row selection validating

• Shuffle before validating

• Random leave out subsets selection

• Multi-fold cross validating

• Comprehensive report

• Prune insignificant weights while learning

• Reduce network to minimum size

• Data grid

• Network nodes and connections

• Learning progress graph

• Column values graph and trends

• Actual and predicted outputs for training and validating examples

• Training and validating examples errors

• Input importance and relative importance

• Input sensitivity and relative sensitivity

• Input and output associations

• Diagnostic array

• General information

Help system, online FAQ and user manual
 

Free support for all users

Many ready trained samples