Continuous-time distributed estimation with asymmetric mixing

Discrete-time mobile adaptive networks have been successfully used to model self-organization in biological networks. We recently introduced a continuous-time adaptive diffusion strategy with the goal of better modeling physical phenomena governed by continuous-time dynamics. In the present paper we extend our previous work, proposing a new continuous-time diffusion estimation strategy that allows asymmetric mixing matrices. We prove that the new algorithm is stable and has better convergence properties than stand-alone learning for the case of doubly-stochastic mixing matrices.


Published in:
IEEE Statistical Signal Processing Workshop (SSP), 528-531
Presented at:
Statistical Signal Processing Workshop (SSP), Ann Arbor, MI, USA, August 5-8, 2012
Year:
2012
Publisher:
IEEE
Laboratories:




 Record created 2017-12-19, last modified 2018-09-13


Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)