Title :
A combined gradient learning algorithm for multilayered neural networks
Author :
Guozhong, Zhou ; Yaming, Sun
Author_Institution :
Dept. of Electr. Eng. & Autom., Tianjin Univ., China
fDate :
28 Oct-1 Nov 1991
Abstract :
A combined gradient learning algorithm is developed based on the gradient and the conjugate gradient optimization algorithms. It combines the advantages of the two optimization algorithms and can greatly increase the convergence speed of learning for multilayered neural networks. It does not have a large storage requirement. The authors review the back-propagation model algorithm, the conjugate-gradient-based algorithm, and the combined gradient algorithm. Simulation results for the XOR problem and the SYMMETRY problem are presented
Keywords :
conjugate gradient methods; convergence of numerical methods; learning systems; neural nets; optimisation; EXOR problem; SYMMETRY problem; XOR problem; back-propagation; combined gradient learning algorithm; conjugate gradient optimization; convergence speed; multilayered neural networks; Artificial neural networks; Automation; Control system synthesis; Convergence; Multi-layer neural network; Neural networks; Neurons; Process control; Sun; System identification;
Conference_Titel :
Industrial Electronics, Control and Instrumentation, 1991. Proceedings. IECON '91., 1991 International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-87942-688-8
DOI :
10.1109/IECON.1991.239120
