deep learning autoencoder network, trained by a nonnegativity constraint algorithm. The
learning algorithm aims to constrain the negative weights, learns features that amount to a
part-based representation of data, and disentangles a more meaningful hidden structure.
The performance of this algorithm is tested on the fourth-order cumulants of the modulated
signals. The results indicate that the autoencoder with nonnegativity constraint (ANC) …