output layer

The result of SVD is the input and the number of classification equals the number of output-layer nodes.

After studying network, coefficient matrix of each unit which includes input layer, intermediate layer and output layer was gained.

Changing this to TRUE tells PHP to tell the output layer to flush itself automatically after every output block.

With the pull methodology, individual nodes have no way of knowing if they are in the input, hidden, or output layer.

Input layer performs import of samples, hidden layer extracts model characters of samples and output layer presents classification results.

Through the analysis of character features, confirm the input layer, hidden layer and the License of output layer units.

BP neural network topology, including the input layer (input), hidden layer (hide layer) and output layer (output layer).

This is because the only parameters that are adjusted in the learning process are the linear mapping from hidden layer to output layer.

Its hidden layer and output layer are composed of process neuron and the hidden layer function consists of wavelet function.

The output layer on layer is to the role of linear combination, can say the results as summarize the result.

And the weight from hide layer neuron to output layer neuron were determined to use pseudo-inverse method.

A speech training method using output-layer visualization of Self-Organizing Map(SOM) is proposed.

This model includes the input layer, hidden layer and output layer.

Efficient Learning Approach for Feedforward Neural Networks Based on the Analytic Modification for the Weights of Output Layer

A Stable Arithmetic for the Output-layer Learning Rate of RBFN