Complex Valued Neural Network

Complex-valued neural networks (CVNNs) extend traditional neural networks by utilizing complex numbers for weights, inputs, and outputs, aiming to improve performance in applications involving complex-valued data like signals and images. Current research focuses on developing effective complex-valued activation functions, adapting backpropagation algorithms to the complex domain, and exploring various architectures including convolutional and transformer networks for tasks such as signal processing, image reconstruction, and classification. The ability of CVNNs to directly handle complex data, without the information loss associated with real-valued representations, makes them a powerful tool with significant implications across diverse scientific and engineering fields.