WAVELET-LIKE ARCHITECTURE OF COMPLEX-VALUED CONVOLUTIONAL NEURAL NETWORK FOR COMPLEX SIGNAL SYNTHESIS
The paper proposes the architecture of a complex-valued convolutional neural network, built upon a structure of the discrete wavelet transform. This architecture allows performing multiscale decomposition of a complex signal, thus forming a set of features that can be used for the tasks of signal synthesis and classification. The article presents the results of solving the prediction problem for the values of a chaotic complex signal by a neural network based on the proposed architecture. The obtained results are compared with the results of solving this problem using real-valued neural networks based on alternative modern approaches. The analysis of the presented complex-valued convolutional network in the frequency domain is also carried out, which was achieved due to a relatively small number of adaptive parameters.