Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data.
The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task. It can learn either from available training patterns or automatically learn from examples or input-output relations. The learning process is designed by one of the following:
- Knowing about available information
- Learning the paradigm--having a model from the environment
- Learning rules--figuring out the update process of weights
- Learning the algorithm--identifying a procedure to adjust weights by learning rules
There are four basic types of learning rules:
- Error correction rules
- Boltzmann
- Hebbian
- Competitive learning
