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Chinese researchers use a powerful new technique to describe mathematical features of a network.
Further, many of the desirable features of a network topology can best be expressed in linguistic terms, which is the basis of fuzzy logic.
In this work, we try to identify the optimal topologies for maximizing the network transmission capacity, as well as to build a clear relationship between structural features of a network and the transmission performance in terms of traffic delivery.
In the context of neural networks, the feature extraction technique enables the application of learned features of a network, trained on a large dataset, on a new problem, by freezing the parameters of all but the last layers, and retraining only them.
"Dynamic" implied fluidity in multiple features of a network, but principally its membership, contrasting it with a fixed network.
The term "network capabilities" refers to what the network is capable of, i.e. the main features of a network such as the QoS, its range, its location, its type (GSM, UMTS), etc.
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A correlation between degrees of two nodes connected by a link is another feature characteristic of a network architecture.
These features are characteristic of a network structure formed by the GG-borax crosslinks.
The distinct feature of a network with a spin wave bus is the ability to control the inter-cell communication by an external global parameter — magnetic field.
One characteristic feature of a network is its degree distribution N(k).
Generally, degree distribution is believed to be a central feature of a network and a key predictor of its dynamic behavior in other respects [5].
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