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Neural networks for data fusion.

Wang, Fengzhen.

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Neural networks for data fusion.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Neural networks for data fusion./
Author:	Wang, Fengzhen.
Description:	183 p.
Notes:	Adviser: J. Litva.
Contained By:	Dissertation Abstracts International59-08B.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ30179
ISBN:	9780612301795

Neural networks for data fusion.
Wang, Fengzhen.

Neural networks for data fusion. - 183 p.

Adviser: J. Litva.

Thesis (Ph.D.)--McMaster University (Canada), 1997.

A feature-mapping neural network for centralized data fusion is presented, and its performance is compared with that of the Maximum Likelihood approach.

ISBN: 9780612301795Subjects--Topical Terms:

769149
Artificial Intelligence.

Neural networks for data fusion.
LDR:03478nam 2200337 a 45 001 945712
005 20110523
008 110523s1997 ||||||||||||||||| ||eng d
020 $a 9780612301795
035 $a (UMI)AAINQ30179
035 $a AAINQ30179
040 $a UMI $c UMI
100 1 $a Wang, Fengzhen. $3 870699
245 1 0 $a Neural networks for data fusion.
300 $a 183 p.
500 $a Adviser: J. Litva.
500 $a Source: Dissertation Abstracts International, Volume: 59-08, Section: B, page: 4356.
502 $a Thesis (Ph.D.)--McMaster University (Canada), 1997.
520 $a A feature-mapping neural network for centralized data fusion is presented, and its performance is compared with that of the Maximum Likelihood approach.
520 $a The process of data fusion may be viewed as a multi-level hierarchical inference process whose ultimate goal is to assess a mission situation and identify, localize and analyze threats. Each succeeding level of data fusion processing deals with a greater level of abstraction. The lower level is concerned solely with individual objects where sensor data are processed to derive the best estimates of current and future positions for each hypothesized object as well as provide an inference as to the identity and key attributes of the objects. With the recent proliferation and increasing sophistication of new technologies, it is recognized that the incorporation of new techniques, such as neural networks and others, will make the data fusion system more powerful in tri-service (command, control and communications).
520 $a In this thesis, optimization neural networks are investigated. A new technique of measurement data association in a multitarget radar environment is developed. The technique is based on the mean-field-theory machine and has the advantages of both the Hopfield network and the Boltzmann machine. In the technical development, three new energy functions have been created. Theoretically, the critical annealing temperature is found to determine the annealing temperature range. A convergence theorem for the mean-field-theory machine is put forward. Based on the technique, neural data association capacities have been evaluated in cases with and without clutter, taking into account different accuracies for radar measurements.
520 $a New energy functions have been extended to multiple dimensional data association. A comprehensive analysis by computer simulations has demonstrated that the new technique developed here possesses high association capacity in the presence of false alarms; it can cope with track-crossing in a dense target environment.
520 $a In support of our study of multisensor data fusion for airborne target classification with artificial neural networks (ANNs), we designed a neural classifier. Multilayer perceptions neural networks trained by back-propagation (BP) rule are discussed. In order to speed up the training or decrease the number of epoches in the learning process, both momentum and adaptive learning rate methods are used. The simulation results show that the technique of automatic target classification using neural networks has the potential to classify targets.
590 $a School code: 0197.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0544
690 $a 0800
690 $a 0984
710 2 $a McMaster University (Canada). $3 1024893
773 0 $t Dissertation Abstracts International $g 59-08B.
790 $a 0197
790 1 0 $a Litva, J., $e advisor
791 $a Ph.D.
792 $a 1997
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ30179