SYMMETRY-BREAKING IN NONMONOTONIC NEURAL NETWORKS

被引：29

作者：

BOFFETTA, G

MONASSON, R

ZECCHINA, R

机构：

[1] UNIV TURIN, INFN, I-10125 TURIN, ITALY

[2] ENS, PHYS THEOR LAB, F-75231 PARIS 05, FRANCE

[3] POLITECN TORINO, DIPARTIMENTO FIS, I-10129 TURIN, ITALY

[4] UNIV TURIN, DIPARTIMENTO FIS TEOR, I-10125 TURIN, ITALY

来源：

JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL | 1993年 / 26卷 / 12期

关键词：

D O I：

10.1088/0305-4470/26/12/005

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

The optimal performance of a non-monotonic neural network is studied by the replica method. In analogy to what happens in multi-layered networks, we show that replica symmetry breaking (RSB) is required. The distribution of the patterns stabilities, the correlations in the distribution of the internal representation and the optimal capacity per synapse (alpha(c) congruent-to 4.8) are computed with one step Of RSB.

引用

页码：L507 / L513

页数：7

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