Title :
Supervised learning in continuous feedback neural network
Author :
Shi, Yuhui ; He, Zhenya
Author_Institution :
Radio Dept., Southeast Univ., Nanjing, China
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;
Conference_Titel :
Systems Engineering, 1992., IEEE International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-0734-8
DOI :
10.1109/ICSYSE.1992.236965
