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 ?
Ahmed proposed the prediction of DO using artificial neural networks [6] and an application of adaptive neurofuzzy inference system (ANFIS) to estimate the DO of Surma River [7].
The prime aim of study was to investigative classification performance of discriminant analysis (DA), artificial neural networks (ANNs) and logistic regression (LR) methods, while they were applied on data of more private academies established or not.
Support vector machine (SVM), which is analytically solved to reach its optimal structural formula, can be represented as a network architecture resembling artificial neural networks (multilayer perceptrons) that have been pruned to obtain model parsimony or improve generalization.
OVERVIEW OF ARTIFICIAL NEURAL NETWORKS AND REGRESSION
After finishing his undergraduate studies, he continued as a postgraduate under the supervision of Geoffrey Hinton, legendary computer scientist and cognitive psychologist, one of the foremost advocates of using artificial neural networks for artificial intelligence.
Several methodologies are available for the analysis of the stability and adaptability of a group of genotypes tested in a series of environments such as the traditional method (CRUZ et al., 2012), PLAISTED & PETERSON (1959), WRICKE (1965), EBERHART & RUSSELL (1966), and based on artificial neural networks (NASCIMENTO et al., 2013) among other methods.
In this context, the use of artificial neural networks (ANNs) is more accurate than linear and nonlinear models (Wang et al., 2010; Yilmaz & Kaynar, 2011; Castro et al., 2017).
Researchers have shown that it is possible to train artificial neural networks directly on an optical chip.
Prediction of the deformation modulus of rock masses using Artificial Neural Networks and Regression methods.

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