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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46186

Title: Computing association probabilities using parallel Boltzmann machines
Authors: Pei-Yih Ting
Ronald A. Iltis
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Concurrent computing;Target tracking;Neurons;Digital audio players;Hopfield neural networks;Filters;Frequency estimation;Circuit simulation;Estimation theory;Frequency measurement
Date: 1993-03
Issue Date: 2018-05-07T06:58:22Z
Publisher: IEEE Transactions on Neural Networks
Abstract: Abstract:
A new computational method is presented for solving the data association problem using parallel Boltzmann machines. It is shown that the association probabilities can be computed with arbitrarily small errors if a sufficient number of parallel Boltzmann machines are available. The probability beta /sub i//sup j/ that the ith measurement emanated from the jth target can be obtained simply by observing the relative frequency with which neuron v(i,j) in a two-dimensional network is on throughout the layers. Some simple tracking examples comparing the performance of the Boltzmann algorithm to the exact data association solution and with the performance of an alternative parallel method using the Hopfield neural network are also presented.
Relation: 4(2), pp.221-233
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46186
Appears in Collections:[資訊工程學系] 期刊論文

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