BACKGROUND TO NEURAL NETWORK
BACKGROUND
The examination of the central nervous system of human brain was the inspiration of neural networks.
In an Artificial Neural Network, simple artificial nodes,known as "neurons", "processing elements" or units",
are connected together to form a network which is calleda biological neural network.
There is no single formal definition of an artificial neural network. However,
a class of statistical or mathematical or computational models
may commonly be called "Neural Networks" if they possess the following
- Consist of sets of adaptive weights, i.e. numerical parameters that are tuned by a learning algorithms, and
- Capable of approximating non-linear functions of their inputs
The adaptive weights are conceptually connection strengths between neurons, which are activated during training and prediction
Neural networks are similar to biological neural networks in performing functions collectively and in parallel by the units,
rather than there being a clear delineation of subtasks to which various units are assigned.
The term "neural network" usually refers to models employed in statistics, cognitive psychology and
artificial intelligence. Neural network models which emulate the central nervous system are part of theoretical neuro science
and computational neuroscience.
Working of Neural Networks
The working of neural networks revolvesaround the myriad of ways these individual neurons can be clustered together.
This clustering occurs in the human mind in such a way that information can be processed in a dynamic, interactive,
and self-organizing way. Biologically, neural networks are constructed in a three-dimensional world from microscopic components.
These neurons seem capable of nearly unrestricted interconnections.
That is not true of in the case of any proposed, or existing, man-made network.
Integrated circuits, using current technology, are two-dimensional devices with a limited number of layers for interconnection.
This physical reality restrains the types, and scope, of artificial neural networks that can be implemented in silicon.
Currently, neural networks are the simple clustering of the primitive artificial neurons.
This clustering occurs by creating layers which are then connected to one another.
How these layers connect is the other part of the "art" of engineering networks to resolve real world problems
