| Application
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Returns application object. Return value: Object. This property is read only.
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| ApplyStoppingConditions
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Select this option to enable early stopping. Return/assignment value: Integer.
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| DisplayRealTimeTrainingGraph
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Use this option to display the real time training graph. Return/assignment value: Integer.
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| ErrorImprovement
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Specifies the minimum reduction in error that the network must achieve within a specified number of training cycles (see NumberOfCyclesForErrorImprovement) before the training is halted. Return/assignment value: Double.
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| GaussRandomizeTheWeights
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Select this option to randomly initialize the weights according to a normal distribution. Return/assignment value: Integer.
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| HiddenLayerActivationFunction
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Specifies the hidden activation functions for Multilayer Perceptron (MLP) or Radial Basis Function (RBF). Return/assignment value: String.
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| HiddenLayerDecayConstant
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Specifies the weight decay constant for regularizing the input-hidden layer weights. MLP neural networks only. Return/assignment value: Double.
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| IncludeTestErrorInRealTimeTrainGraph
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Use this option to include the test error in the real time training graph. Return/assignment value: Integer.
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| IncludeTrainErrorInRealTimeTrainGraph
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Use this option to include the training error in the real time training graph. Return/assignment value: Integer.
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| IterationDialogVisible
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Select this option to make the iteration dialog visible in automation mode. This function is not associated with any dialog options. Assignment value: Integer.
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| LearningRate
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Specifies the learning rate for the gradient descend algorithm. MLP networks only. Return/assignment value: Double.
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| MeanForGaussRandomization
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Specifies the mean/minimum of the distribution (normal or uniform) used to initialize the neural network weights. Return/assignment value: Double.
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| ModelListView
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Specifies the list of active models. Return/assignment value: String.
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| Momentum
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Specifies the momentum for the gradient descend algorithm. MLP networks only. Return/assignment value: Double.
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| Name
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Return value: String. This property is read only.
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| NumberOfCyclesForErrorImprovement
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Specifies the number of training cycles during which the minimum reduction in error must occur (see ErrorImprovement). Return/assignment value: Integer.
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| NumberOfNeuronsForTheHiddenLayer
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Specifies the number of hidden units (neurons) in the hidden layer. Return/assignment value: Integer.
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| NumberOfTrainingCycles
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Specifies the maximum number of cycles to be used for training the neural network. Return/assignment value: Integer.
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| OutputActivationFunction
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Specifies the output activation functions for Multilayer Perceptron (MLP) and Radial Basis Function (RBF). Return/assignment value: String.
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| OutputLayerDecayConstant
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Specifies the weight decay constant for regularizing the hidden-output layer weights. Return/assignment value: Double.
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| Parent
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Returns the parent of the object. Return value: Object. This property is read only.
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| PrintDataStat
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Use this option to generate a statistics spreadsheet for the analysis variables. Return value: Object. This property is read only.
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| ResultsCodes
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Assignment value: Variant
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| ResultsOption
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Assignment value: Integer.
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| ResultsOutputFields
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Assignment value: Variant.
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| ResultsSaveFileName
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Assignment value: String.
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| ResultsSelection
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Assignment value: Variant.
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| ResultsSelectionOption
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Assignment value: Variant.
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| ResultsValues
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Assignment value: Variant.
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| ResultsVariables
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Assignment value: Variant.
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| SeedValueForNetworkInitialization
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Use this option to specify the value of the seed for the random number generator used to initialize the network weights. Return/assignment value: Integer.
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| SummaryOfNetworks
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Print summary spreadsheet of active networks. Return value: Object. This property is read only.
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| TrainingAlgorithm
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Specifies the training algorithm used to train the neural network. MLP networks only. Return/assignment value: String.
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| UniformRandomizeTheWeights
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Select this option to randomly initialize the weights according to a uniform distribution. Return/assignment value: Integer.
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| UseCrossEntropyErrorFunction
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Select this option to use the cross entropy error function for training the neural network. Applicable to classification problems only. Return/assignment value: Integer.
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| UseFixedSeedForNetworkInitialization
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Select this option to use a specified seed value for the random number generator used to initialize the network weights. Return/assignment value: Integer.
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| UseHidWeightDecay
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Select this option to use weight decay regularization for the input-hidden layer weights. MLP neural networks only. Return/assignment value: Integer.
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| UseMultilayerPerceptron
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Select this option to create neural networks of Multilayer Perceptron (MLP) type. Return/assignment value: Integer.
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| UseOutWeightDecay
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Select this option to use weight decay regularization for the hidden-output layer weights. Return/assignment value: Integer.
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| UseRadialBasisFunction
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Select this option to create neural networks of Radial Basis Function (RBF) type. Not available with categorical inputs. Return/assignment value: Integer.
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| UseSumOfSquaresErrorFunction
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Select this option to use the sum of squares error function for training the neural network. Return/assignment value: Integer.
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| VarianceForGaussRandomization
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Specifies the variance/maximum of the distribution (normal or uniform) used to initialize the neural network weights. Return/assignment value: Double.
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