Multi-valued neuron with new learning schemes

Multi-valued neuron (MVN) is an efficient technique for classification and regression. It is a neuron with complex-valued weights and inputs/output, and the output of the activation function is moving along the unit circle on the complex plane. Therefore, MVN may have more functionalities than sigmoidal or radial basis function neurons. In some cases, a pair of weighted sums would oscillate between two sectors and the learning process can hardly converge. Besides, many weighted sums may be located around the borders of each sector, which may cause bad performance in classification accuracy. In this paper, we propose two modifications of multivalued neuron. One is involved with moving boundaries and the other one with targets at the center of sectors. Experimental results show that the proposed modifications can improve the performance of MVN and help it to converge more efficiently.