A new stability condition for discrete time recurrent neural networks with complex-valued linear threshold neurons

This paper discusses the stability condition for discrete time recurrent neural networks (RNNs) with complex-valued linear threshold (CLT) neurons. The energy-function method is very useful for complex-valued RNNs study, especially for multi-stable RNNs. In addition to properties of CLT RNNs discussed in earlier work, a new stability condition is offered here by virtue of a lower-bounded energy function. Simulation results are presented to illustrate the theory.