Stability analysis OS discrete-time recurrently connected neural network
Tianping Chen, Wen Lian Lu · 2004
In this paper, we discuss dynamics of the discrete-time recurrently asymmetrically connected neural networks (DTRACNN). We propose an effective approach to study global stability of the networks. We give some sufficient conditions for the discrete-time recurrently asymmetrically connected neural networks (DRACNN) being exponentially stable. We also give a bound of the step size such that the iteration converges. As a consequence, we derive the exponential stability of continuous-time recurrently asymmetrically connected neural networks (CTRACNN), i.e., the systems that are also controlled by differential equations.