TITLE:
Multi-Valued Neuron with Sigmoid Activation Function for Pattern Classification
AUTHORS:
Shen-Fu Wu, Yu-Shu Chiou, Shie-Jue Lee
KEYWORDS:
Pattern Classification; Multi-Valued Neuron (MVN); Differentiable Activation Function; Backpropagation
JOURNAL NAME:
Journal of Computer and Communications,
Vol.2 No.4,
March
18,
2014
ABSTRACT: 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.