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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/34213

Title: Adaptive algorithms and variable structures for distributed estimation
Authors: Li, Leilei
Issue Date: 2009
Publisher: © Leilei Li
Abstract: The analysis and design of new non-centralized learning algorithms for potential application in distributed adaptive estimation is the focus of this thesis. Such algorithms should be designed to have low processing requirement and to need minimal communication between the nodes which would form a distributed network. They ought, moreover, to have acceptable performance when the nodal input measurements are coloured and the environment is dynamic. Least mean square (LMS) and recursive least squares (RLS) type incremental distributed adaptive learning algorithms are first introduced on the basis of a Hamiltonian cycle through all of the nodes of a distributed network. These schemes require each node to communicate only with one of its neighbours during the learning process. An original steady-steady performance analysis of the incremental LMS algorithm is performed by exploiting a weighted spatial–temporal energy conservation formulation. This analysis confirms that the effect of varying signal-to-noise ratio (SNR) in the measurements at the nodes within the network is equalized by the learning algorithm. [Continues.]
Description: A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy at Loughborough University.
URI: https://dspace.lboro.ac.uk/2134/34213
Appears in Collections:PhD Theses (Mechanical, Electrical and Manufacturing Engineering)

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