Phase-based neuron training using evolutionary algorithms

Complex Valued Neural Networks play an increasing role in machine learning due to their capability to characterize intricate problems and to their appropriateness to engineering fields which use complex number or phasor representations. This paper presents a new method for training the Phase-Based Neurons, a new type of Complex Valued Neural Networks that uses just the phase of the complex activation for operation and representation. The paper investigates also the capabilities this type of neural network to solve the N bit parity problem and its efficiency in doing this.