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The second system under examination was a radial basis network.
We prove that a swapped network is Hamiltonian if its basis network is Hamiltonian.
Two ANN are used here: the Multilayer Perceptron Network (MPN) and the Radial Basis Network (RBN).
It is shown that the Radial Basis Network model is superior in this particular case.
A formerly developed model-based radial basis network advisor (RBN-MB) was used for comparison.
In the original article for mapping an external test set it was recommended to use Radial Basis Network.
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Radial basis networks were very stable.
The smallest training error was achieved with radial basis networks.
Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks.
In the present work, radial basis networks with spread values of 3.45, 4.18, 4.31, 4.88, and 5 were investigated.
The best models both individually and collectively are obtained employing radial basis networks, generalized regression, and multilayer perceptron.
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