Any Artificial Neural Network, irrespective of the style and logic of implementation, has a few basic features as given below.
The Artificial Neural Network systems are modelled on the human brain and nervous system.
They are able to automatically extract features without feeding the input by programmer.
Every node of layer in a Neural Network is compulsorily a machine learning algorithm.
It is very useful to implement when solving problems for very huge datasets.
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It can work with incomplete knowledge and may produce output even with incomplete information.
It has fault tolerance which means that corruption of one or more cells of ANN does not prevent it from generating output.
It has the ability to learn events and make decisions by commenting on similar events.
It has Parallel processing capability i.e. ANN have numerical strength that can perform more than one job at the same time.
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Neural Networks have the ability to learn by themselves and produce the output that is not limited to the input provided to them.
The input is stored in its own networks instead of a database; hence the loss of data does not affect its working.
These networks can learn from examples and apply them when a similar event arises, making them able to work through real-time events.
Even if a neuron is not responding or a piece of information is missing, the network can detect the fault and still produce the output.
They can perform multiple tasks in parallel without affecting the system performance
