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.

Usenet newsgroup: news:comp.ai.neural-nets.
References in periodicals archive ?
A typical artificial neural network consists of three layers: input, hidden and output.
Different artificial neurons gather and make a net of the network that is considered as an artificial neural network (table 6).
An Artificial Neural Network Approach for Brain Tumor Detection Using Digital Image Segmentation", International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2(5).
Artificial neural network (ANN) has been applied to recognize the peanut seeds with the precision of 93%.
In current paper, only one artificial neural network provides the dosage coagulant references (RA(s) and RB(s)) for each control closed-loop in points A and B.
Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network, Energy 50: 177-186.
In Yao's case, using an open-source program run through a Cloud server, he trained an artificial neural network to transform digital photographs into striking images that evoke the painterly styles of specific artists.
However, the performance of artificial neural network heavily depends on the choice of the kernel parameters which are necessary to define the model accuracy.
This study investigate two different data-driven models, support vector machines (SVM) and artificial neural network (ANN) in order to modeling lake level variations.

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