Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data. Neural Networks is one of the Data Mining techniques.
The first step is to design a specific network architecture (that includes a specific number of “layers” each consisting of a certain number of “neurons”). The size and structure of the network needs to match the nature (e.g., the formal complexity) of the investigated phenomenon. Because the latter is obviously not known very well at this early stage, this task is not easy and often involves multiple “trials and errors.” (Now, there is, however, neural network software that applies artificial intelligence techniques to aid in that tedious task and finds “the best” network architecture.)
The new network is then subjected to the process of “training.” In that phase, neurons apply an iterative process to the number of inputs (variables) to adjust the weights of the network in order to optimally predict (in traditional terms, we could say find a “fit” to) the sample data on which the “training” is performed. After the phase of learning from an existing data set, the new network is ready and it can then be used to generate predictions.
One of the major advantages of neural networks is that, theoretically, they are capable of approximating any continuous function, and thus the researcher does not need to have any hypotheses about the underlying model, or even to some extent, which variables matter. An important disadvantage, however, is that the final solution depends on the initial conditions of the network, and, as stated before, it is virtually impossible to “interpret” the solution in traditional, analytic terms, such as those used to build theories that explain phenomena.