By Means of Artificial Neural NetworksNeuner, H
As processes increase in complexity, they become less amenable to direct mathematical modeling based on physical laws. In the later half of the 20th century,artificial neural networkshave made inroads into several disciplines with a wide range of applications. An artificial neural network is a networ...
The tremendous increase in computing power since the 1990s now makes it possible to train large neural networks in a reasonable amount of time. This is in part due to Mooreâs Law, but also thanks to the gaming industry, which has produced powerful GPU cards by the millions. The ...
While neural networks (also called “perceptrons”)have been around since the 1940s, it is only in the last several decades where they have become a major part of artificial intelligence. This is due to the arrival of a technique called “backpropagation,” which allows networks to adjust the...
The growing demands of brain science and artificial intelligence create an urgent need for the development of artificial neural networks (ANNs) that can mimic the structural, functional and biological features of human neural networks. Nanophotonics, whi
[Control of walking and running by means of electric stimulation of the midbrain] Biofizika. 1966;11(4):659-66. ML Shik,FV Severin,GN Orlovskiĭ - 《Electroencephalogr Clin Neurophysiol》 被引量: 842发表: 1966年 Neuronal Network Generating Locomotor Behavior in Lamprey: Circuitry, Transmitte...
River flow prediction using artificial neural networks: generalisation beyond the calibration range Artificial neural networks (ANNs) provide a quick and flexible means of creating models for river flow prediction, and have been shown to perform well in c... CE Imrie,S Durucan,A Korre - 《Journal...
Moreover, the vast majority of the reviewed articles are single-agent system based, which means that they only focus on a single building, bypassing urban boundary conditions. The single-agent approach is correct when very few buildings participate in DR programs. However, if a large number of...
Assessing the training process of artificial neural networks (ANNs) is vital for enhancing their performance and broadening their applicability. This paper employs the Monte Carlo simulation (MCS) technique, integrated with a stopping criterion, to const
(4) correlation metric was used as a real model correlation assessment tool in order to prevent overestimated correlation as computed by standard R2 measure Equation (5), [25], which was found to be constrained by its intrinsic sensitivity to the expected and observed values of means and ...