DocumentCode
3425083
Title
A fast learning algorithm for adaptive linear combiner
Author
Tan, Jun ; Cornett, Frank N.
Author_Institution
Dept. of Electr. & Comput. Eng., Tennessee Technol. Univ., Cookeville, TN, USA
fYear
1997
fDate
9-11 Mar 1997
Firstpage
399
Lastpage
403
Abstract
The paper suggests a learning algorithm for adaptive systems and perceptrons different from traditional learning algorithms The weight updating is kept with “instant” input and output signals. The convergence property is discussed. Also, several examples including system identification are given to show its high convergence speed compared with the LMS algorithm
Keywords
adaptive signal processing; adaptive systems; identification; learning (artificial intelligence); least mean squares methods; perceptrons; LMS algorithm; adaptive linear combiner; adaptive systems; convergence property; fast learning algorithm; high convergence speed; input/output signals; perceptrons; system identification; weight updating; Adaptive systems; Algorithm design and analysis; Convergence; Differential equations; History; Least squares approximation; Neural networks; Stability; Steady-state; System identification;
fLanguage
English
Publisher
ieee
Conference_Titel
System Theory, 1997., Proceedings of the Twenty-Ninth Southeastern Symposium on
Conference_Location
Cookeville, TN
ISSN
0094-2898
Print_ISBN
0-8186-7873-9
Type
conf
DOI
10.1109/SSST.1997.581689
Filename
581689
Link To Document