Theory and applications of sparsely interconnected feedback neural networks

This paper presents some developments in the analysis and design of a class of feedback neural networks with sparse interconnecting structure. The analysis results presented make it possible to determine whether a given vector is a stable memory of a neural network and to what extent implementation errors are permissible. The design methods presented allow the synthesis of neural networks with predetermined sparse interconnecting structures with or without symmetry constraints on the interconnection weights. An example is included to demonstrate the applicability of the methodology advanced herein