On the capability of neural networks with complex neurons in complex valued functions approximation

The capability of neural networks with complex neurons to approximate complex valued functions is investigated. A density theorem for complex multilayer perceptrons (MLPs) with a nonanalytic activation function and one hidden layer is proved. The backpropagation algorithms for MLPs with real, complex analytic and complex non-analytic activation functions are compared with a numerical example.<<ETX>>