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You are here : eLibrary : IAHR World Congress Proceedings : 32nd Congress - Venice (2007) : THEME B: Data Acquisition and Processing For Scientific Knowledge and Public Awareness. : A new neural network model for identification miss-vectors of flow field
A new neural network model for identification miss-vectors of flow field
Author : WU LONGHUA
In the course of the measurement velocity field, because of measurement error, there are inevitably miss-vectors in the original flow field which is measured. The neural network has been an important means for its characteristic to identify miss-vector. In this paper, based on simulating the identification process of cerebra to miss-vector; a new Hopfield neural network model of multi-evidence reasoning is founded. And the identification performance of this model is tested by numerical simulation experiment. The experiment result indicates that, compared with other network model of single-evidence reasoning, the new network model of multi-evidence reasoning can simulate cerebra ideation better, it has better identification function.
File Size : 219,907 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 32nd Congress - Venice (2007)
Article : THEME B: Data Acquisition and Processing For Scientific Knowledge and Public Awareness.
Date Published : 01/07/2007
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