Neural Net

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Neural Net

A computerized system designed to imitate human thought. Specifically, neural nets are designed to make or recommend investment decisions in light of changing circumstances in a market. Using neural nets has become more common as computers have become more accessible.
References in periodicals archive ?
Although the decision on the basis of neural network may look and behave like normal software, they are different in principle, since most implementations based on neural networks "learn" and not programmed: the network learns to perform the task, and is not programmed directly.
Artificial neural network (ANN) expresses knowledge of problem solving by distributed memory of link weight between lots of interconnected neurons.
Biologically inspired procedures such as genetic algorithms, ant colony optimization and neural networks (Kramer, 2009) apply to areas where traditional mathematical methods cannot be realized due to the fact that there are not enough resources to find an exact solution.
One of the main limitations of neural network models may occur especially when complex neural networks are trained with limited amount of data.
In this simulation examples of a control system with MPC using neural networks is given.
Patil and Smith obtained data for generating a computerized neural network from a nomogram previously published by Sarah de Ferranti from the Department of Cardiology at Children's Hospital in Boston and Harvard Medical School, and by Nader Rifai from the Department of Laboratory Medicine at Children's Hospital and Harvard Medical School's Department of Pathology.
Neural networks so as fuzzy logic [7] are dealing with important aspects of knowledge representation, reasoning and learning, but in different approaches with their advantages and weaknesses.
Today, Intel launched the Movidius Neural Compute Stick, the worlds first USB-based deep learning inference kit and self-contained artificial intelligence (AI) accelerator that delivers dedicated deep neural network processing capabilities to a wide range of host devices at the edge.
This research work is based upon the prediction of reservoir properties in the inter-well regions using the technique of artificial neural networks.
After training his neural network, he found that in just a few days, it was able to learn quickly, and managed to sort 4,000 pieces with a 90 percent accuracy rate.
Radial basis function (RBF) neural network is able to be used in logistics requirement prediction, and RBF neural networks is developed based on the radial basis functions.
Apple's move comes a few days after Google added support for iPhones and iPads to its TensorFlow neural network.

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