An analysis of self-organising networks based on goal-seeking neurons

The principle involved in applying self-organising architectures to pattern recognition problems is that patterns which share common features are clustered together, with each cluster representing one and only one class. One architecture that follows such a principle is the GSN self-organising architecture (GSN/sup 8/) a Boolean neural network proposed by Filho, Fairhurst and Bisset (1990, 1991). In creating new clusters to store unfamiliar patterns and grouping similar patterns in the same cluster, the GSN/sup 8/ exhibits the typical behaviour of a self-organising architecture. The authors present an analysis of the computational mechanisms of the GSN/sup 8/ architecture, and modifications will be suggested with the aim of improving its performance in practical processing tasks.<
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