State changing of neural networks based on image correlation

Eugene I. Shubnikov

doi:10.1117/12.262580

27 December 1996 State changing of neural networks based on image correlation

Eugene I. Shubnikov

Proceedings Volume 2969, Second International Conference on Optical Information Processing; (1996) https://doi.org/10.1117/12.262580
Event: Second International Conference on Optical Information Processing, 1996, St. Petersburg, Russian Federation

Abstract

The question of state changing for phase space and correlation space of neural networks based on image correlation is considered. The phase space is used for investigation of network behavior and stable. The network behavior is considered as mutual effect of excitation and inhibition of hypothesizes. The model of network behavior and phase portrait of two competitional hypothesis is presented. If a relative excitation and inhibition are linear functions the system is stable and solution is single. The correlation space is connected a network characteristics with pattern images, i.e. with learning process. The energy relief structure of correlation space for image simulation as random field is considered. The top values of absolute and relative depth of energy relief in true attractor point is calculated.

Citation Download Citation

Eugene I. Shubnikov "State changing of neural networks based on image correlation", Proc. SPIE 2969, Second International Conference on Optical Information Processing, (27 December 1996); https://doi.org/10.1117/12.262580

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