Abstract

Systolic architectures for Sanger and Rubner Neural Networks (NNs) are proposed, and the local stability of their learning rules is taken into account based on the indirect Lyapunov method. In addition, these learning rules are improved for applications based on Principal Component Analysis (PCA). The local stability analysis and the systolic architectures for Sanger NN and Rubner NN are presented in a common framework.

Quotes

0 citations in WOS
0 citations in

Journal Title

Journal ISSN

Volume Title

Publisher

Springer, Berlin, Heidelberg

URL external

URI

https://hdl.handle.net/10115/30168

DOI

https://doi.org/10.1007/3-540-45720-8_20

Date

2001

Description

Las Redes Neuronales de Sanger y Rubner son analizadas desde el punto de vista de sistemas dinámicos por lo que se establecen criterios sobre su estabilidad. Se identifica una de sus principales aplicaciones, Análisis de Componente Principales )PCA). Además, se proponen arquitecturas para dichas Redes Neuronales que optimizan la ejecución por máquina de dichas redes. Representan primeras propuesta de algoritmos y arquitectura para Inteligencia Artificial.

Keywords

Hebbian Neural Network , Systolic architectures , Stability , Artificial Intelligence Algorithms , Principal Component Analysis

Citation

Collections

Comunicaciones a Congresos

Endorsement

Review

Supplemented By

Referenced By

Statistics

Views
74
Downloads
80

Bibliographic managers

Document viewer

Select a file to preview:
Reload