Sentence examples for determination of network from inspiring English sources

The LMA has a random behavior in the determination of network weights and biases.

Studies proved this problem arises due to constant initial backoff windows size, which is an important parameter in determination of network behavior.

In this article, the average Kriging standard deviation was used as a criterion for the determination of network density, and the GIS-based approach was analysized.

These include the determination of network architectures, the assignment-of-credit problem, the potential for catastrophic interference, and the falsifiability of the model.

International cost efficiency benchmarking played a central role in informing the Office of Rail Regulation's (ORR) determination of Network Rail's future funding during the 2008 periodic review (PR08) of the company's finances.

The determination of network structure and parameters is very important; some evolutionary algorithms such as genetic algorithm (GA) (Qu1 et al. 2008), back propagation (BP) (Tang and Xi 2008), pruning algorithm (Reed 1993), simulated annealing (de Souto et al. 2002) can be used for this determination.

This model can tackle the common problem encountered by conventional fuzzy back propagation neural network in the determination of the network structure in the number of hidden layers and the number of hidden neurons in each layer.

A sequential learning neural network model proposed by Zhang and Morris [1] is used in this paper to tackle the common problem encountered by conventional feed forward neural networks in the determination of the network structure in the number of hidden layers and the number of hidden neurons in each layer.

Comparing fractions of actually existing links between nodes that have at least k interaction partners with the maximally possible number of edges in both the underlying and randomized networks ρ allows the determination of significant network patterns.

Although atlases have a lesser effect in the determination of brain network properties, they have a great influence on the quantification of brain network properties.

A new general 2-step method is proposed for more accurate determination of the network parameters.