Regression with neural networks

Artificial neural networks (ANN) are mathematical models for the simulation of typical human brain activities such as image perception, pattern recognition, language understanding, sense-motor coordination, and so on. These models are composed of a system of nodes, equivalent to the neurons of a human brain, which are interconnected by weighted links, equivalent to the synapses between neurons. The output of the network is modified iteratively from link weights to convergence. The original data is provided to the input layer and the result of the network is returned from the output level. The input nodes represent the independent or predictor variables that are used to predict the dependent variables, that is, the output neurons. According to these assumptions, it is clear that this tool can also be used to solve linear and nonlinear regression problems.

Neural network regression is the process of training a neural network on a set of inputs in order to produce...