Multi-Valued Neuron with Sigmoid Activation Function for Pattern Classification


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.

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Wu, S. , Chiou, Y. and Lee, S. (2014) Multi-Valued Neuron with Sigmoid Activation Function for Pattern Classification. Journal of Computer and Communications, 2, 172-181. doi: 10.4236/jcc.2014.24023.

Conflicts of Interest

The authors declare no conflicts of interest.


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