artificial neural network

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artificial neural network

(artificial intelligence)
(ANN, commonly just "neural network" or "neural net") A network of many very simple processors ("units" or "neurons"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. The units operate only on their local data and on the inputs they receive via the connections.

A neural network is a processing device, either an algorithm, or actual hardware, whose design was inspired by the design and functioning of animal brains and components thereof.

Most neural networks have some sort of "training" rule whereby the weights of connections are adjusted on the basis of presented patterns. In other words, neural networks "learn" from examples, just like children learn to recognise dogs from examples of dogs, and exhibit some structural capability for generalisation.

Neurons are often elementary non-linear signal processors (in the limit they are simple threshold discriminators). Another feature of NNs which distinguishes them from other computing devices is a high degree of interconnection which allows a high degree of parallelism. Further, there is no idle memory containing data and programs, but rather each neuron is pre-programmed and continuously active.

The term "neural net" should logically, but in common usage never does, also include biological neural networks, whose elementary structures are far more complicated than the mathematical models used for ANNs.

See Aspirin, Hopfield network, McCulloch-Pitts neuron.

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References in periodicals archive ?
All programs used in the ANN training and validation stages were developed in the C programming language using a gcc-gnu compiler and the FANN library (Fast Artificial Neural Network Library) for the Linux operating system UBUNTU.
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An artificial neural network to predict the outcome of repeat prostate biopsies.
In a feed-forward artificial neural network, information will first be presented to the network through the input layer.
Performance evaluation of artificial neural networks for runoff prediction.
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The novelty of the balancing methodology proposed in this work is the application of the artificial neural network to balance rotors.
2-8) indicate that the used artificial neural network can be a very useful and accurate tool for estimation of high-purity alumina ceramics corrosion behaviour in different concentrations HCl aqueous solution.
Therefore, using an artificial neural network model, a non-invasive method, for the first time we attempted to predict CAD combining seventeen cardiovascular risk factors mentioned above.
The training of ANNs suitable for the current application is accomplished with the cascade correlation algorithm (Fahlman and Lebiere 1990) which produces the Cascade Correlation Artificial Neural Network (CCANN) that belongs to the feed-forward type, which is a supervised algorithm for multilayer feed-forward ANNs (Fig.
Szczypula, 1994, "Comparative Study of Artificial Neural Network and Statistical Models for Predicting Student Grade Point Averages," International Journal of Forecasting, 10(1), June, 17-34.
An artificial neural network is just an attempt to imitate how the brain's networks of nerves learn.

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