Artificial Neural Network

Artificial Neural Network
Synonyms
Neural network
Definition
An analytic modeling technique modeled after the
(hypothesized) processes of learning in the cognitive
system and the neurological functions of the brain.
It is capable of predicting new observations (on specific
variables) from other observations (on the same
or other variables) after executing a process of socalled
learning from existing data. Artificial neural networks
(ANN) are nonlinear and capable of modeling
extremely complex functions by creating connections
between processing elements – the computer equivalent
of neurons. For example, the onset of a particular
medical condition could be associated with a very
complex (e. g., nonlinear and interactive) combination
of changes on a subset of the variables being monitored
(e. g., a combination of heart rate, levels of various
substances in the blood, respiration rate). Neural
networks have been used to recognize this predictive
pattern so that the appropriate treatment can be prescribed.
A distinction can be made between two different
types of ANN– networks designed for supervised
learning tasks (e. g., Multilayer Perceptron, Bayesian
networks, Genetic algorithms) and networks primarily
designed for unsupervised learning (Self Organizing
Feature Map (SOFM, or Kohonen) networks).

Tags: , , , , , , , , , , , , , , , , , , ,

Artificial Neural Network

Artificial Neural Network
Synonyms
Neural network
Definition
An analytic modeling technique modeled after the
(hypothesized) processes of learning in the cognitive
system and the neurological functions of the brain.
It is capable of predicting new observations (on specific
variables) from other observations (on the same
or other variables) after executing a process of socalled
learning from existing data. Artificial neural networks
(ANN) are nonlinear and capable of modeling
extremely complex functions by creating connections
between processing elements – the computer equivalent
of neurons. For example, the onset of a particular
medical condition could be associated with a very
complex (e. g., nonlinear and interactive) combination
of changes on a subset of the variables being monitored
(e. g., a combination of heart rate, levels of various
substances in the blood, respiration rate). Neural
networks have been used to recognize this predictive
pattern so that the appropriate treatment can be prescribed.
A distinction can be made between two different
types of ANN– networks designed for supervised
learning tasks (e. g., Multilayer Perceptron, Bayesian
networks, Genetic algorithms) and networks primarily
designed for unsupervised learning (Self Organizing
Feature Map (SOFM, or Kohonen) networks).

Tags: , , , , , , , , , , , , , , , , , , ,

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