DocumentCode
3315345
Title
Supervised learning in continuous feedback neural network
Author
Shi, Yuhui ; He, Zhenya
Author_Institution
Radio Dept., Southeast Univ., Nanjing, China
fYear
1992
fDate
17-19 Sep 1992
Firstpage
560
Lastpage
563
Abstract
Two supervised learning algorithms for the continuous feedback neural network (CFNN) have been derived which can learn multiple patterns correctly. It is noted that, when the parameters of the CFNN are determined, its dynamic behavior is completely determined, so determining the weight coefficients of the CFNN is a crucial step for using the CFNN. For a CFNN used as associative memory, one wants the required patterns to be the equilibrium points of the CFNN
Keywords
content-addressable storage; learning systems; neural nets; associative memory; continuous feedback neural network; equilibrium points; multiple pattern learning; supervised learning; weight coefficient determination; Associative memory; Capacitors; Differential equations; Helium; Intelligent networks; Neural networks; Neurofeedback; Neurons; Output feedback; Supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems Engineering, 1992., IEEE International Conference on
Conference_Location
Kobe
Print_ISBN
0-7803-0734-8
Type
conf
DOI
10.1109/ICSYSE.1992.236965
Filename
236965
Link To Document