Abstract
The Hopfield neural network is applied to the tracking algorithm of individual particles in the PIV measurement of fluid flows. In order that the particle tracking should work well with relatively large numbers of individual particles, the object function of the network is assigned with some elaborately defined constraint conditions. The new network is applied to synthetic particle images (the VSJ's PIV standard images) as well as experimental images from the water channel experiment.
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