Fully Coupled and Feedforward Neural Networks with Complex-valued Neurons
Speaker(s) : Jacek M. ZURADA (University of Louisville, Kentucky, USA)
This talk introduces a definition of complex-valued neurons with discrete outputs. It covers a novel method of their applications in fully coupled associative memories. Such memories are able to process multiple gray levels when applied for image de-noising. In addition, complex-valued neurons can be generalized to take a continuum of values. Learning of such neurons is demonstrated and described in the context of traditional multilayer feedforward network learning. Such learning is derivative-free and it usually requires reduced network architecture. Selected examples and applications of such networks are also discussed.
Javier.Diaz (at) nulllip6.fr