The computational power of complex-valued neuron

Authors:
Tohru Nitta
Affiliations:
National Institute of Advanced Industrial Science and Technology, Tsukuba-shi, Ibaraki, Japan
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

Citing 3
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Abstract

There exist some problems that cannot be solved with conventional usual 2-layered real-valued neural networks (i.e., a single real-valued neuron) such as the XOR problem and the detection of symmetry. In this paper, it will be proved that such problems can be solved by a 2-layered complex-valued neural network (i.e., a single complex-valued neuron) with the orthogonal decision boundaries. Furthermore, it will be shown that the fading equalization problem can be successfully solved by the 2-layered complex-valued neural network with the highest generalization ability.