Complex functional networks

Functional networks are a recently introduced extension of neural networks, which deal with general functional models instead of sigmoidal-like ones. In this paper, we propose complex functional networks, whose inputs, outputs, neural functions and arguments are all complex-valued. The general learning algorithm for this kind of complex functional networks is derived. And the performance of the proposed complex functional networks is demonstrated with application in the identification of complex-valued communication channels.