Abstract:
Neural network ensemble techniques have
been shown to be very accurate classification
techniques. However, in some real-life applications,
a number of classifiers required to achieve a
reasonable accuracy is enormously large and hence
very space consuming. This paper introduces special
neural method, Parametric Vector Neural Network
(VNN), which has great associative memory and high
performance. Parametric VNN analyzed using
various size of database having randomly created
patterns, noise levels, and fixed q-dimensions. The
result shows that it has capacity much greater than
conventional Neural Networks. Once T matrix is
created for the stored patterns in Database, most
similar pattern with the input one can be achieved
easily by just multiplying two matrices. The resulting
associative memory can recognize highly noisy and
correlate input patterns.
