A new stability condition for discrete time linear threshold recurrent neural networks
Wei Zhou, Jacek M. Żurada · 2014
This paper discusses the stability condition for discrete time recurrent neural networks (RNNs) with linear threshold (LT) neurons. In the existing research literature [1], the LT RNN in synchronous update mode is completely convergent if I-W is a copositive matrix. However, this condition also requires that W should be symmetrical. Here, a new stability condition is presented, which extends previous theoretical result first published in [1], and allows LT RNN to be stable when W is unsymmetrical in some cases. Simulation results are used to illustrate the theory.