Improving error tolerance of self-organizing neural nets

Author

Sha, Fei ; Gan, Qiang

Author_Institution

Lab. of Molecular & Biomolecular Electron., Southeast Univ., Nanjing, China

fYear

1991

fDate

18-21 Nov 1991

Firstpage

2716

Abstract

A hybrid neural net (HNN) combining the network introduced by G.A. Carpenter and S. Grossberg (1987, 1988) and the Hopfield associative memory (HAM) is developed. HAM diminishes noise in samples and provides ART1 samples as inputs. In order to match the capacity of HAM with that of ART1, a new recalling algorithm (NHAM) is also introduced to enlarge the capacity of HAM. Based on NHAM and HNN, a revised version of HNN (RHNN) is introduced. The difference between RHNN and HNN is that RHNN has feedback loops, while HNN has only feedforward paths. The ART1 in RHNN supplies information for HAM to recall memories. Computer simulation demonstrated that RHNN has several advantages

Keywords

content-addressable storage; neural nets; self-adjusting systems; ART1; Hopfield associative memory; error tolerance; feedback loops; feedforward paths; hybrid neural net; recalling algorithm; self-organizing neural nets; Computer simulation; Feedback loop; Filters; Forward error correction; Gallium nitride; Hopfield neural networks; Impedance matching; Molecular electronics; Neural networks; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1991. 1991 IEEE International Joint Conference on

Print_ISBN

0-7803-0227-3

Type

conf

DOI

10.1109/IJCNN.1991.170279

Filename

170279