Hierarchical data matrix pattern recognition and identification system

Abstract

The present invention relates to a hierarchical artificial neural network (HANN) for automating the recognition and identification of patterns in data matrices. It has particular, although not exclusive, application to the identification of severe storm events (SSEs) from spatial precipitation patterns, derived from conventional volumetric radar imagery. To identify characteristic features a data matrix, the data matrix is processed with a self organizing network to produce a self organizing feature space mapping. The self organizing feature space mapping is processed to produce a density characterization of the feature space mapping. The self organizing network is preferably completely unsupervised. It may, under some circumstances include a supervised layer, but it must include at least an unsupervised component for the purposes of the invention. The "self organizing feature space" is intended to include any map with the self organizing characteristics of the Kohonen Self Organizing Feature Map. The frequency vector of a CAPPI image that has been derived is a data abstraction that can be displayed directly for examination. In preferred embodiments, it is presented to a classification network, e.g. the standard CPN network, for classifying the density vector representation of the three dimensional data and displaying a representation of classified features in the three dimensional data. A novel methodology is preferably used for incorporating vigilance and conscience mechanisms in the forward counterpropagation network during training.

Dependencies are inferred from the claim text.