Loughborough University
Browse
Thesis-2009-Li.pdf (5.42 MB)

Adaptive algorithms and variable structures for distributed estimation

Download (5.42 MB)
thesis
posted on 2018-07-31, 10:47 authored by Leilei Li
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.]

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Publisher

© Leilei Li

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2009

Notes

A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy at Loughborough University.

Language

  • en

Usage metrics

    Mechanical, Electrical and Manufacturing Engineering Theses

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC