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In this research, we used a 3-layered network: an input layer with eight units and a hidden layer with five units.
A four-layered backpropagation neural network with an input layer, two hidden layers and one output layer is found optimal.
An MLP consists of three layers: an input layer, an output layer, and an intermediate or hidden layer.
The ANN used here was a three-layered feed-forward neural network consisting of an input layer, a hidden middle layer and an output layer.
Figure 1 shows typical three-layered perceptron as a basic structural architecture with an input layer, a single hidden layer, and an output layer [19].
An DNN consists of multiple fully-connected layers: an input layer, one or multiple hidden layers and a single output layer [27].
There is an input layer, an output layer, and sandwiched between them a so-called hidden layer.
This consists of an input layer, a hidden layer and an output layer.
The dataset is fed into the GMDH model through an input layer.
Each MLP has an input layer, one hidden layer, and one output.
It consists of an input layer, one or more hidden layer(s), and a output layer.
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CEO of Professional Science Editing for Scientists @ prosciediting.com