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Sliding-Window Moving Average Learning for System with Lossy Packets

Asirvadam , Vijanth Sagayan and Saad ., Nordin and Elamin Jabralla, Musab and McLoone, Sean (2010) Sliding-Window Moving Average Learning for System with Lossy Packets. Australian Journal of Intelligent Information Processing Systems, 11 (4). pp. 8-15. ISSN 1321-2133

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Official URL: http://cs.anu.edu.au/ojs/index.php/ajiips

Abstract

Wireless technologies tend to become a core element of acquisition and sensory system and system identification process represents an important tool in many practical engineering applications. The current trend is to integrate this lossy network technologies and system identification together by having an identification element (identifier) that is able to give a good description for a system underlying dynamic when the system observations input/output data) are sent wirelessly (with lost packets). The lossy network normally sent input-output data (packets) with irregular sample periods thus introduces challenges in system identification process. This paper investigates the possibility of performing system identification with irregular sample time using first order linear system with sliding window moving average techniques. By adopting data store management approach on sliding window of data the recursive identification methods proposed are able to map the black-box system with irregular stream of sample with good level of accuracy.

Item Type:Article
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:3809
Deposited By: Dr Vijanth Sagayan Asirvadam
Deposited On:04 Jan 2011 00:38
Last Modified:01 Apr 2014 06:08

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