Phase model reduction for oscillatory networks subject to stochastic inputs - Normandie Université Access content directly
Conference Papers Year : 2012

Phase model reduction for oscillatory networks subject to stochastic inputs

Abstract

Oscillatory networks represent a circuit architecture for image and information processing, that can be used to realize associative and dynamic memories. Phase noise is often a limiting key factors for the performances of oscillatory networks. The ideal framework to investigate phase noise effect in nonlinear oscillators are phase models. Classical phase models lead to the conclusion that, in presence of random disturbances such as white noise, the phase noise problem is simply a diffusion process. In this paper we develop a reduced order model for phase noise analysis in nonlinear oscillators. We derive a reduced Fokker-Planck equation for the phase variable and the corresponding reduced phase equations. We show that the phase noise problem is a convection-diffusion process, proving that white noise produces both phase diffusion and frequency shift.
No file

Dates and versions

hal-02047779 , version 1 (25-02-2019)

Identifiers

Cite

Michele Bonnin, Fernando Corinto, Valentina Lanza. Phase model reduction for oscillatory networks subject to stochastic inputs. 2012 13th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2012), Aug 2012, Turin, Italy. pp.1-6, ⟨10.1109/CNNA.2012.6331422⟩. ⟨hal-02047779⟩
15 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More