List of Main Keywords
- ACTIVATION function of neurons.
- TRAINING Indicates method that will be
used to train neural network weights.
Seven methods are currently available.
ANALYSIS Indicates method that will be used
to analyze data.
Two methods, i.e. to fit data and to prune input variables, are currently
ASNN Indicate to use Associative Neural Network algorithm for data analysis.
- AVOID Indicates input variables that will not
be considered in the analysis.
- The variables indicated in this keyword will be deleted from the input
data set before any analysis.
CLASSIFICATION Performs classification on classes and not the regression
- CORRELATION Level of correlation to eliminate
highly correlated input variables.
- The second of each two highly correlated variables (with R^2>value
of this keyword) will be deleted before any analysis.
DISTANCE function for the ASNN algorithm.
- ENSEMBLE Indicates the number of neural networks
that will be averaged.
- Analysis of several networks estimates variability of calculated results.
INCLUDE Indicates input variables that will
be considered in the analysis.
- The default value is to include all variables.
the number of input (independent) variables for neural network calculations.
- This parameter should be identified for all calculations.
ITERATIONS Maximal number of iterations for
neural network calculations.
- The network calculations will be stopped if maximal number of iterations
KNN Maximal number of nearest neighbors for the ASNN algorithm.
- LIMIT Indicates RMSE error to stop neural network
- A neural network calculation is stopped if RMSE error for the learning
set is less than value of this keyword.
LOO Performs validation of the approach using the
- A data entry is removed and its value predicted after neural network
calculations. This procedure is repeated for all data entries.
MISSED If several output values are analyzed simultaneously, the missed
values should be substituted with the this value.
- MODELS Indicates if neural network weight should
- This keyword is useful to save neural network models for further analysis
of new data.
if the first column of data table contains names.
- Names should not contain spaces and tabs.
NONZERO Indicate minimal number of non constant elements in a column.
- NEURONS Determines hidden layers of neural network.
- The numbers of neurons in each hidden layer are indicated.
OUTLIERS absolute value to detect outliers in the output values. Mainly
used to calculate statistic with and without outliers.
number of outputs (dependent) variables for neural network calculations.
- This number determines number of neurons on the last layer of neural
PARALLEL Uses several computers (if available) to speed-up calculations.
- PARTITION Indicates method to subdivide data
on training and validation subsets.
- The initial training data set is divided on training and validation
set according to this criterion.
PRINT Indicates options for output information.
- The corresponding checkbox should be checked to activate each option.
PRUNE Performs optimization of input variables
using pruning methods.
- The variables will be pruned according to their sensitivities calculated
by neural network ensemble.
RANGE Initial range for weight initialization.
that the reversed order of input and target values is used in the data
- The standard order is X, independent variables, are followed by Y,
targets or dependent variables.
SEED Random seed number.
- This number is used in to start sequence of random numbers for neural
SHOW Number of iterations to print detailed information
about neural network calculations.
- This keyword can be useful to select number of iterations required
for neural network training..
TYPE Type of neural network.
- Two methods, namely feed-forward back propagation and cascade correlation,
are currently available.
UPDATE mode: either after each sample or using the all samples (batch).
- VALIDATION Indicates which data entries (rows)
will be used as validation data set.
- Explicitly indicate which data entries should be used as the validation
that should be always verified.
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