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

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Abstract Straight Forward Constructive Deep Learning Neural Network (SFC-DLNN) algorithm is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology, SFC-DLNN begins with a minimal network (perceptron), then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, the input-side weights of the new architecture are generated. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating others, more complex feature space is then created where the data is likely to be linearly separable. The SFC-DLNN architecture has several advantages over existing algorithms: it learns quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes.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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