摘要：Multi-Valued Neuron (MVN) was proposed for
pattern classification. It operates with complex-valued 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 compare
the performance of MVN-sig with that of MVN. The experimental results show a
good potential to develop a multilayer networks based on MVN-sig.
