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Relational Matching Using a Neural Network

โœ Scribed by Noboru Katta; Hideya Takahashi; Kenji Matsushita; Eiji Shimizu

Book ID
104591313
Publisher
John Wiley and Sons
Year
1990
Tongue
English
Weight
549 KB
Volume
21
Category
Article
ISSN
0882-1666

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โœฆ Synopsis

Abstract

This paper proposes an algorithm for relational matching, which is important in pattern recognition, by using the Hopfield model as one of the neural network models. First, the objective function for relational matching is derived and the computer simulation of the neural networking for relational matching according to this objective function is conducted by use of a simple model. Next, the local minima in connection with the Hopfield model is avoided by using triangular noise and its effectiveness is verified in the network for relational matching. This method differs from the conventional simulated annealing method and, by adding noise to the network, the network will reach only one stationary state for which results will be derived by this method.

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