DocumentCode
2432231
Title
Real-time pattern recognition. I. Neural network algorithms for normal models
Author
Mallya, Satyanarayana ; Jannarone, Robert
Author_Institution
Dept. of Electr. & Comput. Eng., South Carolina Univ., Columbia, SC, USA
fYear
1991
fDate
10-12 Mar 1991
Firstpage
580
Lastpage
583
Abstract
Traditional neural network models, when contrasted with normal models, have some distinct similarities and differences. The paper describes the salient features of perceptrons and their extensions, relative to normal models. Conjunctoid neural network models when contrasted with normal models also have some relative advantages, the paper describes these advantages and disadvantages. Normal neural network models are introduced which combine some of the features of the conjunctoid and traditional normal models. The paper also introduces the algorithm to implement a normal neural network model for the airplane example described in an earlier part of the paper. The paper briefly describes the architectures for implementing the algorithms on parallel machines, along with conclusions and future research directions
Keywords
computerised pattern recognition; neural nets; parallel algorithms; parallel architectures; conjunctoid models; neural network models; normal models; parallel machines; Airplanes; Algorithm design and analysis; Covariance matrix; Libraries; Neural networks; Parameter estimation; Pattern recognition; Predictive models; Statistics; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
System Theory, 1991. Proceedings., Twenty-Third Southeastern Symposium on
Conference_Location
Columbia, SC
ISSN
0094-2898
Print_ISBN
0-8186-2190-7
Type
conf
DOI
10.1109/SSST.1991.138634
Filename
138634
Link To Document