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The last two decades have seen rapid developments in computing taking as their inspiration the human
brain. The human brain functions in a highly parallel and distributed fashion. The adaptive structure of
the brain means that learning or training can accompany decision making.
This basic neural model has inspired computer hardware exhibiting a parallelism which has revolutionised
processing speeds in complex task analysis. Similarly there has been substantial activity in the field of
intelligent software and in particular in the area ofneural computing.
The human brain may viewed as composed of approximately 1 dbasic units, the neurons. Each neuron
exhibits a high degree of interconnectivity with connections to approximately 1 O other neurons. Each
neuron accepts many inputs which are added or integrated in some fashion and this causes the neuron to
become active or passive. The active neuron emits an output to interconnected neurons. The importance
of any one input is controlled by the effectiveness of the corresponding interconnection or weight.
One area that has attracted attention in the application of neural networks is pattern recognition. Here the
functions of feature classification and extraction are handled by a network which receives some education
or training prior to the task of recognition. A priori knowledge of expected outcomes is used as a starting
point with the network being allowed to modify or enlarge its knowledge base as the task proceeds.
Various models or approaches to adaptive problem solving have been developed.
The pattern recognition problem considered in the present paper is the identification of image grouping in
double exposure PIV images. The aim is to provide an adaptive net which, following initial training, is
able to identify image partners and adapt to changing flow conditions. This latter feature is seen as
essential in order that the full potential of the neural net in temporally or spatially changing flow regimes can be realised.
An important class of neural network is the multi-layer perceptron. The neurons are distributed on
surfaces and linked by weighted interconnections. In the present paper we demonstrate how this type of
net can developed into a competitive, adaptive filter which will identify PIV image pairs in a number of
commonly occurring flow types.
Previous work by the authors in particle tracking analysis (1, 2) has shown the efficiency of statistical
windowing techniques in flows without systematic (in time or space) variations. The effectiveness of the
present neural net is illustrated by applying it to digital simulations ofturbulent and rotating flows.
Work reported by Cenedese et al (3) has taken a different approach in examining the potential for neural
net methods applied to PIV.
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