Applications

The utility of artificial neural network models lies in the fact that they can be used to infer a function from observations and also to use it. This is particularly useful in applications where the complexity of the data or task makes the design of such a function by hand impractical.
Real life applications
The tasks to which artificial neural networks are applied tend to fall within the following broad categories:
Function approximation, or regression analysis, including time series prediction and modelling.
Classification, including pattern and sequence recognition, novelty detection and sequential decision making.
Data processing, including filtering, clustering, blind signal separation and compression.
Application areas include system identification and control (vehicle control, process control), game-playing and decision making (backgammon, chess, racing), pattern recognition (radar systems, face identification, object recognition, etc.), sequence recognition (gesture, speech, handwritten text recognition), medical diagnosis, financial applications, data mining (or knowledge discovery in databases, "KDD"), visualization and e-mail spam filtering.
Use in Teaching Strategy
Neural Networks are being used to determine the significance of a seating arrangement in a classroom learning environment. In this application, neural networks have proven that there is a correlation between the location of high and low-performing students in the room and how well they do in the class. An article in Complexity explains that when low-performing students are seated in the front, their chance to do better increases. The results of high-performing students who are seated in the back are not affected. In addition, when high-performing students are seated in the outer four corners, the performance of the class as a whole increases.