Proceedings PaperContribution of the number of elements in the hidden layer in a back-propagation network to overall decision accuracy
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The back-propagation network (BPN) has a minimum of one hidden layer of processing elements between the input and output layers. The addition of a hidden layer, or layers, along with the generalized delta rule, are responsible for the BPN exceeding the linear restrictiveness of the earlier Perceptron. Although the major significance of the hidden layer has been well-established, there is no general agreement on a method for determining the number of hidden elements to use for a given data set. One avenue to increasing the window of choosing the optimal number of elements in the hidden layer is to better understand how the number of hidden elements contributed to decision accuracy. In the present research, a single hidden layer BPN was trained using Anderson's classic IRIS data set and tested with a 10-fold validation method across separate studies. While holding all BPN parameters constant, 19 separate tests were conducted beginning with two hidden elements and increasing to 20 hidden elements.