Share Email Print
cover

Proceedings Paper

Techniques for high-performance analog neural networks
Author(s): David P. Casasent; Leonard Neiberg; Sanjay S. Natarajan
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

We consider analog neural network implementations (using VLSI or optical technologies) with limited accuracy and various noise and nonlinearity error sources. Algorithms and techniques to achieve high performance (good recognition P'c% and large storage capacity) on such systems are considered. The adaptive clustering neural net (ACNN) and robust Ho-Kashyap (HK-2) associative processor (AP) are the neural networks considered in detail.

Paper Details

Date Published: 1 March 1992
PDF: 14 pages
Proc. SPIE 1608, Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods, (1 March 1992); doi: 10.1117/12.135106
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)
Leonard Neiberg, Carnegie Mellon Univ. (United States)
Sanjay S. Natarajan, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 1608:
Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods
David P. Casasent, Editor(s)

© SPIE. Terms of Use
Back to Top