Consider a gym center. It receives periodic enrollments, but also, unfortunately, it receives periodic lock enrollments. Imagine that a gym’s administrator wished to predict for each new client the period of time that would elapse between his enrollment and his enrollment lock. With this “future forecast” in hand he would be able to do a job of convincing those whose abandonment was expected soon. This information, however, would only be useful if the moment of detachment were known well in advance. If possible, already on enrollment day.
Another situation: a telephone company. Whenever a customer delays their payment for a few days, the company control system automatically triggers a postal charge, generating costs. Considering that some of these late payment users are not bad payers, but forgotten customers or traveling customers who will pay their bills as soon as they remember or return from travel, this cost from postal charge would not have to occur. The most effective would be to send collection only to real bad payers.
Is it possible the company to separate the mass of customers into two groups, good payers and bad payers?
These two cases are real and have been solved with the use of artificial neural networks (ANNs), a branch of artificial intelligence (or, as it is more modernly called, computational intelligence).
If you are just wanting to satisfy your curiosity about ANNs, or even wanting to acquire an initial foundation that allows you to deepen your knowledge then this book may be a good option.
Otherwise, if you have surpassed the initial phase of studies and already have the mathematical foundation behind the ANNs concepts, look for a more in-depth bibliography, such as that available at the end of this book.
Here, the goal will be to open the door to the basic understanding of ANNs.