artificial neural network

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artificial neural network

(ar-ti-fish'ăl nū'răl net'wŏrk),
a computer-based decision-making system for complex data sets comprising processor nodes interconnected in a weighted fashion, simulating a biologic nervous system.
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Study shows the importance of getting rid of unwanted outliers in the source data when using artificial neural networks (Jerome et al.
Artificial neural networks are a branch of the field known as Artificial Intelligence.
According to Haykin (1999), Artificial Neural Networks (ANN) are massively parallel networks, are self-adaptive and are interconnected by basic structures called neurons.
Using artificial neural networks has been recognized widely as a way in predicting hourly energy usage of buildings.
The use of artificial neural networks in decision support in vesicoureteral reflux treatment.
Artificial Neural Networks [10] have been applied to image compression problems, due to their superiority over traditional methods when dealing with noisy or incomplete data.
For many different problems with difficult or impossible finding of formal algorithms to solve them, artificial neural networks can be applied (Saloky et al.
Uncertainty treatment using paraconsistent logic; introducing paraconsistent artificial neural networks.
This study proposes an alternative approach to solving areal interpolation problems by using artificial neural networks.
Artificial neural networks have an inherent ability to learn and recognised highly nonlinear relationships (Swingler, 1996), and then organise dispersed data into a nonlinear model (Hecht, 1989).
Artificial Neural Networks is gaining importance and is a powerful tool in analyzing these natural currents of HVDC converter transformer due to its excellent pattern recognition technique and pattern recognition capability.

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