Multi-Valued Neuron with Sigmoid Activation Function for Pattern Classification

Multi-Valued Neuron (MVN) was proposed for pattern classification. It operates with complex-va-lued inputs, outputs, and weights, and its learning algorithm is based on error-correcting rule. The activation function of MVN is not differentiable. Therefore, we can not apply backpropagation when constructing multilayer structures. In this paper, we propose a new neuron model, MVN-sig, to simulate the mechanism of MVN with differentiable activation function. We expect MVN-sig to achieve higher performance than MVN. We run several classification benchmark datasets to com-pare the performance of MVN-sig with that of MVN. The experimental results show a good poten-tial to develop a multilayer networks based on MVN-sig