Bounds of the incremental gain for discrete-time recurrent neural networks
Yun-Chung Chu · Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228) · 2003
As a nonlinear system, a recurrent neural network generally has an incremental gain different from its induced norm. While most of the previous research efforts were focused on the latter, this paper presents a method to compute an effective upper bound of the former for a class of discrete-time recurrent neural networks, which is not only applied to systems with arbitrary inputs but also extended to systems with small-norm inputs. The upper bound is computed by simple optimizations subject to linear matrix inequalities.