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The models were also optimized to identify the suitable network topology and training method.
In this work we are seeking for the optimizing results, which will be considered by choosing the suitable network parameters.
The genetic algorithm implemented with neural network to determine automatically the suitable network architecture and the set of parameters from a restricted region of space.
For the nomadic mobile service hosted on a multi-homed handheld mobile device, context-awareness provides a capability of selecting the suitable network interface for the data transfer.
Two different ANFIS networks (by changing the training optimization algorithms) were compared with evaluation of networks accuracy in bubble point pressure prediction and subsequently the suitable network was determined.
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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.
Finally, the Levenberg Marquardt algorithm with the six neurons in the hidden layer has been found to be the most suitable network.
The number of epochs was 1,000 for training, and the most suitable network configuration found was 4 × 24 × 1.
Counter-Propagation Artificial Neural Networks (CP-ANNs) require an optimisation step in order to choose the most suitable network architecture.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com