Abstract: | This paper studies the global output convergence of a class of recurrent delayed neural networks with time-varying inputs.
We consider non-decreasing activations which may also have jump discontinuities in order to model the ideal situation where
the gain of the neuron amplifiers is very high and tends to infinity. In particular, we drop the assumptions of Lipschitz
continuity and boundedness on the activation functions, which are usually required in most of the existing works. Due to the
possible discontinuities of the activations functions, we introduce a suitable notation of limit to study the convergence
of the output of the recurrent delayed neural networks. Under suitable assumptions on the interconnection matrices and the
time-varying inputs, we establish a sufficient condition for global output convergence of this class of neural networks. The
convergence results are useful in solving some optimization problems and in the design of recurrent delayed neural networks
with discontinuous neuron activations. |