If you can work with a spreadsheet program, you will be able to train a neural network. Set the number of data variables you have and the number of desired outputs and you re ready to go. Sample training files illustrate each of the available network algorithms. Look up information on the user interface, the algorithms, and a complete function reference in the on line help files. Data Preprocessing Input preprocessing makes network training faster and gives better solutions so frequently, that we built it into the software.
Different inputs can have different dynamic ranges of values.
The error graph shows the performance of the network training at every pass through the data. Most neural network training tools use mean squared error as the basis of their learning rules. Mean Squared Error tends to overreact to outliers and ignore some training examples in favor of a refined solution on others. By using Mean Absolute Error , the network can becomes less sensitive to outlying data points. With Mean Fourth Power Error , the network tries to minimize the worst performing training example.