| Network type
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Options in this group box specify the type of network (multilayer perceptron or radial basis function).
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| Multilayer perceptron (MLP)
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Generates multilayer perceptron networks. The multilayer perceptron is the most common form of network. It requires iterative training and the networks are quite compact, execute quickly once trained, and in most problems yield better results than the other types of networks.
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| Radial basis function (RBF)
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Generates radial basis function networks. Radial basis function networks tend to be slower and larger than multilayer perceptron and often have inferior performance, but they can be trained faster than MLP for large data sets and linear output activation functions.
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| Error function
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Specifies the error function to be used in training a network.
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| Sum of squares
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Generates networks using the sum of squares error function. Note that this is the only error function available for regression type analyses.
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| Cross entropy
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Generates networks using cross entropy error functions. This error function assumes that the data is drawn from the exponential family of distributions and supports a direct probabilistic interpretation of the network outputs. Note that this error function is only available for classification problems. The option is unavailable for regression type analyses. When the Cross entropy error function is selected, the Output neurons (in the Activation functions group box) are set to Softmax.
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| Activation functions
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Select activation functions for the hidden and output neurons. The choice of the activation function, i.e., the precise mathematical function, is crucial in building a neural network model since it is directly related to the performance of the model. It is a good practice to choose the tanh and identity functions for the hidden and output neurons for multilayer perceptron networks (default settings) when the
Sum of squares error function is used. For radial basis function networks, the Hidden units are automatically set to Gaussian; and the Output units are set to either Identity (when Sum of squares error function is used) or Softmax (when Cross entropy error function is used).
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| Hidden units
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Use the Hidden units drop-down list to select the activation function for the hidden layer neurons. For multilayer perceptron networks, these include the identity function, hyperbolic tanh (recommended), logistic sigmoid, exponential, and sine activation functions. For radial basis functions networks, a Gaussian activation function is always used for hidden neurons.
- Identity: Uses the identity function. With this function, the activation level is passed on directly as the output.
- Tanh: Uses the hyperbolic tangent function (recommended). The hyperbolic tangent function (tanh) is a symmetric S-shaped (sigmoid) function, whose output lies in the range (-1, +1). Often performs better than the logistic sigmoid function because of its symmetry.
- Logistic: Uses the logistic sigmoid function. This is an S-shaped (sigmoid) curve, with output in the range (0, 1).
- Exponential: Uses the exponential activation function.
- Sine: Uses the standard sine activation function.
- Gaussian: Uses a Gaussian (or Normal) distribution. This is the only choice available for RBF neural networks.
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| Output units
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Use the Output units drop-down list to select the activation functions for the hidden-output neurons. For multilayer perceptron networks, these include the identity function (recommended), hyperbolic tanh, logistic sigmoid, exponential, sine, and softmax activation functions. For Radial basis function (RBF) networks, the choice of Output units is dependent on the selected Error function. For RBF networks with Sum of squares error function, an Identity activation function is used. For RBF networks with Cross entropy error function, the Softmax activation function is always used.
- Identity: Uses the identity function (recommended). With this function, the activation level is passed on directly as the output.
- Tanh: Uses the hyperbolic tangent function. The hyperbolic tangent function (tanh) is a symmetric S-shaped (sigmoid) function, whose output lies in the range (-1, +1). Often performs better than the logistic sigmoid function because of its symmetry.
- Logistic: Uses the logistic sigmoid function. This is an S-shaped (sigmoid) curve, with output in the range (0, 1).
- Exp.: Uses the negative exponential activation function.
- Sine: Uses the standard sine activation function.
- Gaussian: Uses a Gaussian (or Normal) distribution. This is the only choice available for RBF neural networks.
- Softmax: Uses a specialized activation function for one-of-N encoded classification networks. It performs a normalized exponential (i.e., the outputs add up to 1). In combination with the cross entropy error function, it allows multilayer perceptron networks to be modified for class probability estimation.
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| Networks to train
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Specifies the number of networks the Custom Neural Network (CNN) should train. The larger the number of networks trained, the more detailed is the search carried out by the CNN. It is recommended that you set the value for this option as large as possible depending on your hardware speed and resources. Although you can create one type of network type at a time, by training more than one network you can find multiple solutions provided by the same network. Furthermore, using the Results dialog, you can combine the predictions of these networks to create ensembles. Using predictions drawn from an ensemble of networks can generally yield better results compared to the predictions of the individual networks.
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| No. of neurons
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Specifies the number of neurons in the hidden layer of the network. The more neurons the hidden layer contains, the more complex (flexible) it becomes.
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