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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Nitta, T. (2003). The computational power of complex-valued neuron. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 993–1000. https://doi.org/10.1007/3-540-44989-2_118
Mendeley helps you to discover research relevant for your work.