Difference between revisions of "Artificial neural network"

Revision as of 17:32, 13 March 2016

In machine learning and cognitive science, artificial neural networks (ANNs), or simply neural networks, are a family of models inspired by biological neural networks.

Description

Artificial neural networks are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown.

They are generally presented as systems of interconnected "neurons" which exchange messages between each other.

The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning.

Handwriting recognition

For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image.

After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons.

This process is repeated until finally, an output neuron is activated.

This determines which character was read.

Other applications

Like other machine learning methods -- systems that learn from data -- neural networks have been used to solve a wide variety of tasks that are hard to solve using logic programming, including computer vision and speech recognition.

See also

• Artificial intelligence
• Computer science
• Data mining

External links

• Artificial neural network @ Wikipedia