A new state space model for a complex RTRL neural network
Pedro Henrique Gouvêa Coelho · 2002
The purpose of the work is to represent the complex real time recurrent learning (RTRL) fully recurrent neural network in a state space model for engineering applications such as mobile channel equalization. This representation extends Haykin's (1999) for complex valued inputs, yielding a compact formulation useful in possible changes in the training of a fully recurrent neural network. Numerical results are presented to illustrate the method.