A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
This paper establishes a nonlinear separation principle based on contraction theory and derives sharp stability conditions for recurrent neural networks (RNNs). First, we introduce a nonlinear separation principle that guarantees global exponential stability for the interconnection of a contracting state-feedback controller and a contracting observer, alongside parametric extensions for robustness and equilibrium tracking. Second, we derive sharp linear matrix inequality (LMI) conditions that guarantee the contractivity of both firing rate and Hopfield neural network architectures. We establish structural relationships among these certificates-demonstrating that continuous-time models wit...
| Authors | Anand Gokhale, Anton V. Proskurnikov, Yu Kawano et al. |
|---|---|
| Primary category | eess.SY |
| Published | 16 April 2026 |
| Last updated at source | 29 April 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.