Influence of Self-Connection Weights on Cellular-Neural Network Stability
by Sergey Pudov
Abstract:
Cellular-Neural Associative Memory (memory by Hophield with local connection structure) with weight matrix designed by anyone of the existing methods ensuring individual stability of network is concidered. It is studied how self-connection weight values influence the main characteristic of CNAM, namely the strong stability to k-distortions of stored prototypes. Expression for determining the self-connection weight values is obtained, such that provides a maximal strong stability for each prototype. Two strategies are proposed to determine the most acceptable value according to the requiered accuracy. The obtained results are valid not only for CNAM but also for full-connected Hopfield associative memory designed with the help of any learning method.
Keywords: theory
Source:
S. Pudov, Influence of Self-Connection Weights on Cellular-Neural Network Stability. In V. Malyshkin (ed.),
Parallel Computing Technologies: Proceedings of the 4th International Conference,
Lect. Notes in Comp. Sci., Vol. 1277, Springer, 1997, pp. 76-82
