This brief discusses a class of discrete-time recurrent neural networks with complex-valued linear threshold neurons. It addresses the boundedness, global attractivity, and complete stability of such networks. Some conditions for those properties are also derived. Examples and simulation results are used to illustrate the theory.
No takes yet. Share an insight, caveat, or question.
Zhou et al. (2009) studied this question.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: