Use this URL to cite or link to this record in EThOS:
Title: Real-time applications of artificial neural networks
Author: D'Souza, Winston Anthony
ISNI:       0000 0001 3401 3166
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2007
Availability of Full Text:
Access from EThOS:
This research takes an innovative look at two distinct applications of Artificial Neural Networks (ANNs) concerning the manipulation of data within real-time systems. The first contribution of this research involves the filtering of errors associated with data emergent from Inertial Navigation Systems (INS) by adopting an ANN filter.  This novel approach when compared to present day optimal estimation filter techniques for random data such as the Kalman Filter (KF) and its variants, offers a better estimated response without the need to mathematically model error.  In addition to this advantage, due to its inherent properties of effortlessly handling nonlinear data, the ANN filter eliminates the need to convert such data into their linear forms thereby maintaining the integrity of the original data.  Furthermore, since the ANN filter is considerably more economical compared to a KF, it makes itself a likely candidate for low-cost applications.  Results from this research have indicated that the performance of an ANN filter when used for real-time applications within INS, offers a similar degree of accuracy of estimation as well as shorter correction times for such signals compared to the former.  ANN filters though, are not “plug-n-play” devices but require adequate training before they can function reliably and independently of any aiding or correction source (e.g. Kalman Filters).  However, with the continual growth in their knowledge and increased training, they perfect their correction ability considerably. The second contribution of this research was in the area of on-line data compression.  This innovative approach builds on the strengths of present day compression schemes.  However, unlike current schemes that continually compresses data (such as web pages) transmitted through a network every time they are requested, the ANN scheme points to data they may already be pre-compressed and stored within the client’s memory at an earlier stage.  If clients request such data that has already been pre-compressed using this scheme, these are decompressed locally from its resident memory.  If clients do not hold a pre-compressed page due to it being unknown or is an updated version of the web page in its memory, it downloads the web page using the contemporary on-line real-time compression scheme (e.g. mod­_gzip). With this approach therefore, a client’s browser does not have to download every web page requested from the Internet but just the previously unseen ones thereby reducing user perceived latency.  Results from this research have indicated that the ANN scheme for on-line data compression is fairly reliable in correctly recognising web pages previously used for training though there have been some difficulties with regard to web pages not adopting the standard 128 character ASCII code such as Unicode.  As this scheme presently operates entirely in software, it offers some difficulties with regard to the recognition time involved in identifying web pages previously browsed or even unseen by the user.  It is hoped that this problem will be mitigated if this scheme is migrated into hardware using these parallel processors.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available