THE USE OF NEURAL TECHNIQUES IN PIV AND PTV

The neural network method uses ideas developed from the physiological modelling of the human brain in computational mechanics. The technique provides mechanisms analogous to biological processes such as learnin g from experience, generalizing from learning data to a wider set of s timuli and extraction of key attributes from excessively noisy data se ts. It has found frequent application in optimization, image enhanceme nt and pattern recognition, key problems in particle image velocimetry (PIV). The development of the method and its principal categories and features are described, with special emphasis on its application to P IV and particle tracking velocimetry (PTV). The application of the neu ral network method to important categories of the PIV image analysis p rocedure is described in the present paper. These are image enhancemen t, fringe analysis, PTV and stereo view reconciliation. The applicatio ns of common generic net types, feed-forwards and recurrent, are discu ssed and illustrated by example. The key strength of the neural techni que, its ability to respond to changing circumstances by self-modifica tion or regulation of its processing parameters, is illustrated by exa mple and compared with conventional processing strategies adopted in P IV.