System identification using neural networks and higher order statistics

The objective of this dissertation is to use neural network technology, in conjunction with second order statistics and higher order statistics, to identify signal models. Classical system identification method has always been based on the assumption that the observed signal is Gaussian. This type o...

Full description

Saved in:
Bibliographic Details
Main Author: Ngai, Chee Leong.
Other Authors: Chen, Lihui
Format: Theses and Dissertations
Language:English
Published: 2009
Subjects:
Online Access:http://hdl.handle.net/10356/19677
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-19677
record_format dspace
spelling sg-ntu-dr.10356-196772023-07-04T16:02:01Z System identification using neural networks and higher order statistics Ngai, Chee Leong. Chen, Lihui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The objective of this dissertation is to use neural network technology, in conjunction with second order statistics and higher order statistics, to identify signal models. Classical system identification method has always been based on the assumption that the observed signal is Gaussian. This type of system can be identified using the first and second order statistics, i.e mean and covariance sequence. Master of Science (Communications and Computer Networking) 2009-12-14T06:21:12Z 2009-12-14T06:21:12Z 1995 1995 Thesis http://hdl.handle.net/10356/19677 en NANYANG TECHNOLOGICAL UNIVERSITY 150 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Ngai, Chee Leong.
System identification using neural networks and higher order statistics
description The objective of this dissertation is to use neural network technology, in conjunction with second order statistics and higher order statistics, to identify signal models. Classical system identification method has always been based on the assumption that the observed signal is Gaussian. This type of system can be identified using the first and second order statistics, i.e mean and covariance sequence.
author2 Chen, Lihui
author_facet Chen, Lihui
Ngai, Chee Leong.
format Theses and Dissertations
author Ngai, Chee Leong.
author_sort Ngai, Chee Leong.
title System identification using neural networks and higher order statistics
title_short System identification using neural networks and higher order statistics
title_full System identification using neural networks and higher order statistics
title_fullStr System identification using neural networks and higher order statistics
title_full_unstemmed System identification using neural networks and higher order statistics
title_sort system identification using neural networks and higher order statistics
publishDate 2009
url http://hdl.handle.net/10356/19677
_version_ 1772827522511142912