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Counter-Propagation Artificial Neural Networks (CP-ANNs) require an optimisation step in order to choose the most suitable network architecture.
Finally, the Levenberg Marquardt algorithm with the six neurons in the hidden layer has been found to be the most suitable network.
In such a context, mobile devices are equipped with multiple radio interfaces allowing connectivity to the most suitable network environment based on users' requirements and operators' policies.
Levenberg Marquardt backpropagation training function with six neurons in hidden layer was found to be the most suitable network and the coefficient of determination (R2) was 99.5%.
The most suitable network architecture of the CP-ANN was chosen using a genetic algorithm optimisation procedure for global optimisation and to avoid chance results caused by random initialisation.
In addition, this model can be used as a tool to understand the effects of the main network parameters on the network energy consumption, and to find the most suitable network configuration for a given application scenario.
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The GAL network is arguably the most suitable gene network to test our model due to the network's well-characterized [ 14– 18] nature in terms of its components and their interaction topology.
This paper contains a systematic review of the most suitable communication network topologies, technologies and protocols for smart microgrids.
It manages dynamic context information and allows mobile devices to be always connected to the most suitable access network.
Therefore, depending upon the application these parameters must be wisely selected to manufacture the most suitable microchannel network that can lead to optimum multifunctional composite structures.
The resulting architecture not only provides a seamless handover but also supports the decision making when searching for the most suitable target network.
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