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.
Highlights
The discrete multi-valued neuron (MVN) was proposed by N
Wu et al In this paper, we propose a new neuron model, Multi-Valued Neuron (MVN)-sig, to simulate the mechanism of MVN with differentiable activation function
We can see that MVN-sig achieves a better testing accuracy than MVN
Summary
The discrete multi-valued neuron (MVN) was proposed by N. Two discrete-valued MVN learning algorithms are presented in [2] They are based on error-correcting learning rule and are derivative-free. This makes MVN have higher functionality than sigmoidal or radial basis function neurons. MLMVN learning rule is heuristic error backpropagation due to the fact that the activation function of MVN is not differentiable. (2014) Multi-Valued Neuron with Sigmoid Activation Function for Pattern Classification. We propose a new neuron model, MVN-sig, to simulate the mechanism of MVN with differentiable activation function. We can obtain a differentiable input/output mapping and we apply a naive gradient descent method as its learning rule.
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