Author(s)

Alexey Minin, Alois Knoll, Hans-Georg Zimmermann

Siemens AG, Corporate Technology, München, Germany.
Technische Universit?t München—Robotics and Embedded Systems, München, Germany.

Recurrent Neural Networks were invented a long time ago, and dozens of different architectures have been published. In this paper we generalize recurrent architectures to a state space model, and we also generalize the numbers the network can process to the complex domain. We show how to train the recurrent network in the complex valued case, and we present the theorems and procedures to make the training stable. We also show that the complex valued recurrent neural network is a generalization of the real valued counterpart and that it has specific advantages over the latter. We conclude the paper with a discussion of possible applications and scenarios for using these networks.

Complex Valued Neural Networks; Complex Valued System Identification; Recurrent Neural Networks; Complex Valued Recurrent Neural Networks

Minin, A. , Knoll, A. and Zimmermann, H. (2012) Complex Valued Recurrent Neural Network: From Architecture to Training. Journal of Signal and Information Processing, 3, 192-197. doi: 10.4236/jsip.2012.32026.