| Autor: | B. V. Kryzhanovskii, L. B. Litinskii |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
Quantitative Biology::Neurons and Cognition
Artificial neural network Computer science business.industry Small number Content-addressable memory Condensed Matter::Disordered Systems and Neural Networks Autoassociative memory Hopfield network Recurrent neural network Control and Systems Engineering Bidirectional associative memory Artificial intelligence Electrical and Electronic Engineering business Algorithm Parametric statistics |
| Zdroj: | Automation and Remote Control. 64:1782-1793 |
| ISSN: | 0005-1179 |
| DOI: | 10.1023/a:1027386531462 |
| Popis: | The Hopfield model effectively stores a comparatively small number of initial patterns, about 15% of the size of the neural network. A greater value can be attained only in the Potts-glass associative memory model, in which neurons may exist in more than two states. Still greater memory capacity is exhibited by a parametric neural network based on the nonlinear optical signal transfer and processing principles. A formalism describing both the Potts-glass associative memory and the parametric neural network within a unified framework is developed. The memory capacity is evaluated by the Chebyshev–Chernov statistical method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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