Design and analysis of high-capacity associative memories based on a class of discrete-time recurrent neural networks

Zhigang Zeng, Jun Wang

Результат исследований: Вклад в журналСтатьярецензирование

130 Цитирования (Scopus)

Аннотация

This paper presents a design method for synthesizing associative memories based on discrete-time recurrent neural networks. The proposed procedure enables both hetero- and autoassociative memories to be synthesized with high storage capacity and assured global asymptotic stability. The stored patterns are retrieved by feeding probes via external inputs rather than initial conditions. As typical representatives, discrete-time cellular neural networks (CNNs) designed with space-invariant cloning templates are examined in detail. In particular, it is shown that procedure herein can determine the input matrix of any CNN based on a space-invariant cloning template which involves only a few design parameters. Two specific examples and many experimental results are included to demonstrate the characteristics and performance of the designed associative memories.

Язык оригиналаАнглийский
Страницы (с-по)1525-1536
Число страниц12
ЖурналIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Том38
Номер выпуска6
DOI
СостояниеОпубликовано - 2008
Опубликовано для внешнего пользованияДа

Fingerprint

Подробные сведения о темах исследования «Design and analysis of high-capacity associative memories based on a class of discrete-time recurrent neural networks». Вместе они формируют уникальный семантический отпечаток (fingerprint).

Цитировать