Title :
Noise performance of linear associative memories
Author :
Raghunath, Kalavai J. ; Cherkassky, Vladimir
Author_Institution :
Dept. of Electr. Eng., Minnesota Univ., Minneapolis, MN, USA
fDate :
7/1/1994 12:00:00 AM
Abstract :
The performance of two commonly used linear models of associative memories, generalized inverse (GI) and correlation matrix memory (CMM) is studied analytically in the presence of a new type of noise (training noise due to noisy training patterns). Theoretical expressions are determined for the S/N ratio gain of the GI and CMM memories in the auto-associative and hetero-associative modes of operation. It is found that the GI method performance degrades significantly in the presence of training noise while the CMM method is relatively unaffected by it. The theoretical expressions are plotted and compared with the results obtained from Monte Carlo simulations and the two are found to be in excellent agreement
Keywords :
content-addressable storage; neural nets; noise; S/N ratio gain; auto-associative modes; correlation matrix memory; correlation memory; generalized inverse; hetero-associative modes; linear associative memories; linear models; noise performance; training noise; Associative memory; Coordinate measuring machines; Crosstalk; Degradation; Encoding; Multi-layer neural network; Neural networks; Pattern analysis; Performance analysis; Signal to noise ratio;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
