NDLI: Blind Source Recovery in a State-Space Famework: Algorithms for Static and Dynamic Environments

Content Provider	Springer Nature Link
Author	SALEM, FATHI M. WAHEED, KHURRAM ERTEN, GAIL
Copyright Year	2005
Abstract	This paper describes a generalized state space Blind Source Recovery (BSR) framework obtained by using the Kullback-Lieblar divergence as a performance functional and the application of optimization theory under the constraints of a feedforward state space structure. Update laws for both the non-linear and the linear dynamical systems have been derived for the domain of dynamic blind source recovery along both ordinary stochastic gradient and the Riemannian contra-variant gradient directions. The choice of the rich state space demixing network structure allows for the development of potent learning rules, capable of handling most filtering paradigms and convenient extension to non-linear models. Some particular filtering cases are subsequently derived from this general structure and are compared with material in the recent literature. Some of this reported work has also been implemented in dedicated hardware/software. An illustrative simulation example has been presented to demonstrate the online adaptation capabilities of the proposed algorithms.
Starting Page	153
Ending Page	173
Page Count	21
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	21
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2005-01-01
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	adaptive signal processing blind source recovery blind source separation blind source deconvolution natural gradient state space static and dynamic environments stochastic gradient unsupervised learning Artificial Intelligence (incl. Robotics) Nonlinear Dynamics, Complex Systems, Chaos, Neural Networks Electronic and Computer Engineering Operation Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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