DocumentCode
1579177
Title
Adaptive neural network algorithm for solving linear algebra problems
Author
Galushkin, A.I. ; Sudarikov, V.A.
Author_Institution
Sci. Neurocomput. Centre, Acad. of Sci., Moscow, Russia
fYear
1992
Firstpage
128
Abstract
Discusses the construction of adaptive neural network algorithms for solving systems of linear equations and linear inequalities. The performance and efficiency are evaluated for parallel algorithms. An assessment is made of the gain in terms of throughput when neural network algorithms are used in terms of `natural´ implementation. The speed performance of an algorithm is proportional to the number of physically implemented linear threshold elements and may reach 2K
Keywords
computational complexity; linear algebra; mathematics computing; neural nets; parallel algorithms; adaptive neural network algorithms; efficiency; gain; linear algebra; linear equations; linear inequalities; linear threshold elements; parallel algorithms; performance; throughput; Adaptive systems; Appraisal; Concrete; Equations; Linear algebra; Linear matrix inequalities; Neural networks; Optimization methods; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Neuroinformatics and Neurocomputers, 1992., RNNS/IEEE Symposium on
Conference_Location
Rostov-on-Don
Print_ISBN
0-7803-0809-3
Type
conf
DOI
10.1109/RNNS.1992.268602
Filename
268602
Link To Document