Complex Valued

Complex-valued neural networks (CVNNs) extend traditional neural networks by incorporating complex numbers, aiming to improve performance on tasks involving complex-valued data like signals and images. Current research focuses on developing and analyzing CVNN architectures, including those based on radial basis functions, normalizing flows, and specialized activation functions, as well as efficient training algorithms like complex backpropagation. This field is significant because CVNNs offer the potential for enhanced representation and processing of data with inherent complex structure, leading to improved performance in applications such as signal processing, medical imaging, and scientific computing.