An introduction to complex-valued recurrent correlation neural networks

In this paper, we generalize the bipolar recurrent correlation neural networks (RCNNs) of Chiueh and Goodman for complex-valued vectors. A complex-valued RCNN (CV-RCNN) is characterized by a possible non-linear function which is applied on the real part of the scalar product of the current state and the fundamental vectors. Computational experiments reveal that some CV-RCNNs can implement associative memories with high-storage capacity. Furthermore, these CV-RCNNs exhibit an excellent noise tolerance.