This paper presents a new types of complex-valued sigmoid function for a fully multi-layered complex-valued neural network (CVNN). By using the concept of the subordination between analytic functions in open disc, we able to study the reducibility of CVNN. A real-world problem example has been used as a classifier. The simulations results reveal that the proposed fully complex-valued network, been better trained reduces the testing time by 54% compared to the choice of using the traditional sigmoid activation function.
Key words: Complex valued neural network, activation functions, reducibility, irreducibility, subordination.
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