Paper Info
Title
New algorithm for efficient pattern recall using a static threshold with the Steinbuch Lernmatrix
Abstract
An associative memory is a binary relationship between inputs and outputs, which is stored in an M matrix. The fundamental purpose of an associative memory is to recover correct output patterns from input patterns, which can be altered by additive, subtractive or combined noise. The Steinbuch Lernmatrix was the first associative memory developed in 1961, and is used as a pattern recognition classifier. However, a misclassification problem is presented when crossbar saturation occurs. A new algorithm that corrects the misclassification in the Lernmatrix is proposed in this work. The results of crossbar saturation with fundamental patterns demonstrate a better performance of pattern recalling using the new algorithm. Experiments with real data show a more efficient classifier when the algorithm is introduced in the original Lernmatrix. Therefore, the thresholded Lernmatrix memory emerges as a suitable and alternative classifier to be used in the developing pattern processing field.
Year
DOI
Venue
2011
10.1080/09540091.2011.557716
Connect. Sci.
Keywords
Field
DocType
fundamental pattern,thresholded lernmatrix memory,alternative classifier,crossbar saturation,static threshold,steinbuch lernmatrix,efficient pattern,new algorithm,efficient classifier,correct output pattern,original lernmatrix,associative memory,pattern recognition,artificial intelligence,artificial intelligent,classifier
Subtractive color,Content-addressable memory,Binary relation,Computer science,Bidirectional associative memory,Algorithm,Artificial intelligence,Lernmatrix,Classifier (linguistics),Recall,Machine learning,Crossbar switch
Journal
Volume
Issue
ISSN
23
1
0954-0091
Citations 
PageRank 
References 
1
0.36
7
Authors
2
Name
Order
Citations
PageRank
José Juan Carbajal Hernández1269.48
Luis Sánchez23613.87