The use of backpropagating neural networks in coordination with logistic spline coefficients to obtain the change-point

This work presents an algorithm that uses backpropagating neural networks and their ability to find logistic regression coefficients as a useful tool that may outperform other conventional methods for finding the change-point of a data set. To seek this objective, we first demonstrate the capacity of a one-layered neural network to find logistic regression coefficients. Then, we add another layer to the network in order to determine if two-layer discriminants would allow us to approximate the change-point. We then develop an algorithm based on hidden layer coefficients of neural networks that allows us to assess change-points based on when a neural network switches from one "neuron" to another.