SANN - Custom Neural Network/Subsampling - RBF Tab

Select the RBF tab of the SANN - Custom Neural Network dialog box or the SANN - Subsampling dialog box to access the options described here. For information on the options that are common to all tabs (located at the top and on the lower-right side of the dialog box), see SANN - Custom Neural Network. Note that the RBF tab is only available when the Radial basis functions (RBF) option button is selected on the Quick (MLP/RBF) tab of the SANN - Custom Neural Network dialog box or the Quick tab of the SANN - Subsampling dialog box.

Note: The options on this tab affect the hidden-output layer weights only (i.e., the weights connecting the radial basis functions in the hidden layer to the outputs of the RBF network) and only when the output activations are other than the identity function. The basis functions centers and spreads are fixed in a separate training process. See Network Training for more details.

Cycles. In the Cycles field, specify the number of training cycles for the network. In each training cycle the entire training set is passed through the networks and the network error  is calculated. This information is then used to adjust the weights so that the error is further reduced. Note that for RBF networks, this option is only available for classification type analyses when the Cross entropy error function has been selected on the Quick (MLP/RBF) tab.

Stopping conditions. Use the options in this group box to specify when to apply the stopping conditions for early stopping of network training. Note that for RBF networks, these options are only available for classification type analyses when the Cross entropy error function has been selected on the Quick (MLP/RBF) tab. Note: Although SANN defaults to using the training set for early stopping when no test sample is selected, it is still possible to use the training set for that purpose while also having a test sample. To do so, select a validation set (instead of a test set). The only difference between test and validation sets is that the former is used for early stopping while the latter is never presented to the network while training is in progress. So, by having a validation set, you effectively have a test set while using the training sample for early stopping.

Apply stopping conditions. Select the Apply stopping conditions check box to implement early stopping to the training of the neural network. Early stopping is applied when the conditions defined below are met by the training algorithm.

Change in error. When stopping conditions are applied, network training will end if the average network error improvement over a specified number of training cycles (see next entry) is less than the Error improvement value given here.

Window. In the Window field, enter the number of training cycles over which the average improvement in network error must be at least as large as the specified Error improvement (see above).

Network initialization. Use the options in this group to specify how the weights should be initialized at the beginning of training. You can select Normal randomization or Uniform randomization. In addition to selecting a distribution, you must also specify the mean/min and variance/max to use. You may change the default mean/min and variance/max settings if you want, but it is generally recommended that you set the mean/min to zero and variance/max no more than 0.1. This will help the network to gradually grow from its linear state (small weight values) to the nonlinear (large weight values) mode for modeling input-target relationship as and when necessary during the training process.

Normal randomization. Select the Normal randomization option button to use a normal randomization of weights for the neural network model. A normal distribution (with the mean and variance specified below) will be used to draw the initial weight values.

Uniform randomization. Select the Uniform randomization option button to use a uniform randomization of weights for the neural network model. A uniform distribution (with the mean and variance specified below) will be used to draw the initial weight values.

Mean/Min. In this field, specify either the mean (for the normal distribution) or the minimum value (for the uniform distribution) to use for drawing the initial (i.e., before training starts) weight sample.

Variance/Max. In this field, specify either the variance (for the normal distribution) or the maximum value (for the uniform distribution) to use for drawing the initial (i.e., before training starts) weight sample.