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
