Implement a 2-input 3-unit Backpropagation network for different activation functions on excel (Neural networks)

Implement a 2-input 3-unit Backpropagation network on excel for 11 activation functions

Table 2a contains a training set, Table 2b a validation set and Table 2c a test set.

Your experiments must include the following:

a) Learning rates of 0.05, .1, .25, .5, .75 and 1.0.

b) Five replications for each set of learning rates, using different randomly selected initial

values for all of the weights.

c) Plots of sum of square errors after 0, 1, 10, 100, 1000 and 10000 epochs. Use log

scales for both x (number of epochs) and y (sum of errors) axes.

Excel template will be given

Algorithm Excel Matlab and Mathematica Neural Networks Java

Project ID: #24744286

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