Monday, January 21, 2008
What are neural networks?
Neural network, as well known as a parallel dispersed processing network, is a computing paradigm that is generously modeled after cortical structures of the brain. It consists of constant processing elements called nodes or neurons that work jointly to create an output function. The output of a neural network relies on the collaboration of the individual neurons within the network to work. Processing of information by neural networks is characteristically done in parallel somewhat than in series (or sequentially) as in former binary computers or Von Neumann machines. Since it relies on its member neurons jointly to carry out its function, a unique property of a neural network is that it can still get its overall function even if some of the neurons are not functioning. In new words it is robust to tolerate error or failure. In addition, neural networks are more readily adaptable to fuzzy logic computing tasks than are Von Neumann machines.
By tradition, the term neural network has been used to refer to a network of biological neurons. In current usage, the term is time and again used to refer to artificial neural networks, which are collected artificial neurons or nodes. Thus the term 'Neural Network' has two different connotations:
* Biological neural networks are made up of actual biological neurons that are connected or functionally-related in the peripheral nervous system or the central nervous system
* Artificial neural networks are made up of interconnecting artificial neurons (generally simplified neurons) intended to model (or mimic) some properties of biological neural networks.
