Artificial complex neurons with half-plane-like and angle-like activation function

The paper deals with the problems of Boolean functions realization on neural-like units with complex weight coefficients. The relation between classes of realizable function is considered for half-plane-like activation function. We also introduce the concept of sets separability, corresponding to our notion of neuron. The iterative online learning algorithm is proposed and sufficient conditions of its convergence are given. We also consider complex neurons with angle-type activation functions.