NEURAL NETWORKS using MATLAB. FUNCTION APPROXIMATION and REGRESSION by K. Taylor

Author:K. Taylor [Taylor, K.]
Language: eng
Format: azw3
Publisher: UNKNOWN
Published: 2017-02-07T05:00:00+00:00

There are other criteria that can be used to stop network training. They are listed in the following table.

Parameter

Stopping Criteria

min_grad

Minimum Gradient Magnitude

max_fail

Maximum Number of Validation Increases

time

Maximum Training Time

goal

Minimum Performance Value

epochs

Maximum Number of Training Epochs (Iterations)

The training will also stop if you click the Stop Training button in the training window. You might want to do this if the performance function fails to decrease significantly over many iterations. It is always possible to continue the training by reissuing the train command shown above. It will continue to train the network from the completion of the previous run.

From the training window, you can access four plots: performance, training state, error histogram, and regression. The performance plot shows the value of the performance function versus the iteration number. It plots training, validation, and test performances. The training state plot shows the progress of other training variables, such as the gradient magnitude, the number of validation checks, etc. The error histogram plot shows the distribution of the network errors. The regression plot shows a regression between network outputs and network targets. You can use the histogram and regression plots to validate network performance.

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