Architectural design and analysis of learnable self-feedback ratio-memory cellular nonlinear network (SRMCNN) for nanoelectronic systems.

Architectural design and analysis of learnable self-feedback ratio-memory cellular nonlinear network (SRMCNN) for nanoelectronic systems.

In this paper, a learnable cellular nonlinear network (CNN) with space-variant templates, ratio memory (RM), and modified Hebbian learning algorithm is proposed and analyzed. By integrating both the modified Hebbian learning algorithm with the self-feedback function and a ratio memory into CNN archi...

Täydet tiedot

Bibliografiset tiedot
Julkaisussa:IEEE Transactions on VLSI systems 12, 11 (2004).
Päätekijä: Lai, J.-L
Aineistotyyppi: Artikkeli
Kieli:English
Aiheet:
A-template weights.
Associative memory.
Cellular nonlinear network.
Constant leakage current.
Feature enhancement.
Image processing.
Modified Hebbian learning algorithm.
Nanoelectronic systems.
Normal distribution.
Pattern recognition.
Resultant ratio memory.
Self feedback ratio memory.
Uniform distribution noise level.