Artificial neural network (ANN), usually called neural network (NN), is a system of mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. A neural network is, in essence, an attempt to simulate the brain. Neural network theory revolves around the idea that certain key properties of biological neurons can be extracted and applied to simulations, thus creating a simulated (and very much simplified) brain. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. Modern neural networks are non-linear statistical data modeling tools. They are usually used to model complex relationships between inputs and outputs or to find patterns in data.

Advantages of Artificial Neural Networks

  • A neural network can perform tasks that a linear program can not.
  • When an element of the neural network fails, it can continue without any problem by their parallel nature.
  • A neural network learns and does not need to be reprogrammed.
  • Neural networks can be implemented in any application.
  • Neural networks can be implemented without any problem.

Disadvantages of Artificial Neural Networks

  • Neural network needs training to operate.
  • The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated.
  • Neural network requires high processing time for large neural networks.

Applications of Artificial Neural Networks

The utility of artificial neural network models lies in the fact that they can be used to infer a function from observations. This is particularly useful in applications where the complexity of the data or task makes the design of such a function by hand impractical. The tasks of artificial neural networks are applied to tend to fall within the following broad categories:

  • Function approximation, or regression analysis, including time series prediction, fitness approximation and modeling.
  • Classification, including pattern and sequence recognition, novelty detection and sequential decision making.
  • Data processing, including filtering, clustering, blind source separation and compression.
  • Robotics, including directing manipulators, Computer numerical control.

Application areas include system identification and control (vehicle control, process control), quantum chemistry, game-playing and decision making (backgammon, chess, racing), pattern recognition (radar systems, face identification, object recognition and more), sequence recognition (gesture, speech, handwritten text recognition), medical diagnosis, financial applications (automated trading systems), data mining (or knowledge discovery in databases, “KDD”), visualization and e-mail spam filtering.