Straight Forward Constructive Deep Learning Neural Network (SFC-DLNN) Algorithm

Abstract

Ndom Francis Rollin, Mveh-Abia Chantal, Ayissi Raoul, Etoua Remy, Emvudu Yves

Straight Forward Constructive Deep Learning Neural Network (SFC-DLNN) algorithm is a new architecturebased algorithm for artificial neural networks. Rather than simply adjusting the weights in a fixed topology network, SFC-DLNN starts with a minimal topology (perceptron), then builds up their network by gradually trains and adds new nodes one by one, creating multiple layers’ network. Once a unit has been added to the network, the weights of the new architecture are generated. This unit then stands as a permanent detector of features in the network, and a more complex feature space is then created where the data is likely to be linearly separable. The SFC-DLNN algorithm has many advantages over existing ones: it has good learning speed, the network determines its topology size, and the structures it has built is retained after the training stage. We obtain from our built model (SFC-DLNN) an accuracy and specificity of 83:5% from a simulated data set using the uniform distribution. This is not the best but is enough to approve the model prediction capacity.

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