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CF models are similar to feed-forwardnetworks,butincludeweightconnectionfrom the input to eachlayerandfromeach layer thesuccessive layers. Whiletwo-layerfeedforwardnetworks can potentially learn virtualanyinputoutputrelationship, feed-forward networks with more layersmight learn complex relationships more quickly. The function
newcf creates cascade-forward networks. For example, a threelayer
network has connections from layer 1 to layer 2, layer 2
to layer 3, and layer 1 to layer 3. The three-layer network also
has connections from the input to all three layers. The
additional connections might improve the speed at which the
network learns the desired relationship [9]. CF artificial
intelligence model is similar to feedforward backpropagation
neural network in using the backpropagation algorithm for
weights updating, but the main symptom of this network is that
each layer of neurons related to all previous layer of neurons
[10].Tan-sigmoid transfer function, log - sigmoid transfer
function and pure linear threshold functions were used to reach
the optimized status[9].The performance of cascade forward
backpropagation and feedforward backpropagation were
evaluated using Root Mean Square Error (RMSE) Eq. (1),
Mean Square Error (MSE) Eq. (2) and R

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