Neural networks overview


An Artificial Neural Network(ANN) is an information processing model that is quality, by the way, the biological nervous system, such as the brain, process information. The key element of this model is the establishing structure of the information processing system.

A Neural network is a group of a huge number of highly interconnected processing elements or neurons.

In Artificial Neural Network is Connectionist systems are computing systems are inspired by the biological neural networks that constitute brains.

For example in image recognition, they might learn to identify images that contain cows by analyzing example images that have been manually labeled “cow” or “no cow” and using the results to identify cows in other images.

Why use neural networks?

Neural networks, with their extraordinary ability to obtain meaning from complicated or lose data, can be used to extract patterns and detect trends that are too complex to be noticed by either computer or humans.

A trained neural network can be thought of as an “authority” in the category of information it has been given to analysis. This authority can be used to provide estimate given new situations of interest and answer “what if ” questions.


Adaptive learning: The capacity to learn how to do tasks based on the data given for instructions or initial experience.

Self Organization: An Artificial Neural Network can create its own organization or representation of the information it receives during learning time.

Real-Time Operation: An Artificial Neural Network can be carried out in parallel, and special hardware devices are planning and make which take a lead of this ability.

Summary: An Artificial Neural Network is an information processing, recognition to identify images. And processing elements like neurons for complicated problems. In ANN have more advantages including adaptive learning, self-organization, fault tolerance and real-time operations for the neural network.

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