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MLP network has one input layer, one or more hidden layers and one output layer with at least one neuron in each layer.
A multilayer percepetron (MLP) network was adopted for the ANN.
Bayesian regularization was used to improve the generalization of the MLP network.
Firstly, flows are modelled with Multi-Layer Perceptron (MLP) network using gradient-based LevenbergMarquardt algorithm.
To our knowledge, this is the highest speed reported to date for any MLP network implementation on FPGAs.
First, an ANN approach is illustrated based on supervised multi-layer perceptron (MLP) network for the electrical consumption forecasting.
Training of each MLP network for the DOHANN model has required less computer time in comparison to SNN model.
The structure of the neural network used in this work is a multi-layer perceptron (MLP) network.
The intracellular description of carbon metabolism is simulated by a multilayer-perceptron (MLP) network to prepare the hybrid-neural model.
In this way, multi-layer perception (MLP) network was used for non-linear mapping between the input and output parameters.
A two-layer MLP network with twelve neurons in its hidden layer has been designed as the best configuration.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com