New activation functions for
complex-valued neural network
Hamid A. Jalab1*
and Rabha W. Ibrahim2
of Computer System and Technology, Faculty of Computer
Science and Information Technology, University Malaya, 50603
Kuala Lumpur, Malaysia.
of Mathematical Sciences, Faculty of Science and Technology,
Universiti Kebangsaan Malaysia, Bangi 43600, Selangor Darul
*Corresponding author. E-mail:
Accepted 14 March, 2011.
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.