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Wilson, E. (1994) Backpropagation Learning for Systems with Discrete-Valued Functions. Proceedings of the World Congress on Neural Networks, San Diego, California, June.

has been cited by the following article:

• 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.