Title :
A Hybrid Supervised Neural Network Learning Algorithm
Author :
Weng, Pin-Hsuan ; Liu, Fang-Tsung ; Chen, Yu-Ju ; Chang, Chuo-Yean ; Hwang, Rey-Chue
Author_Institution :
Dept. of Electr. Eng., I-Shou Univ., Kaohsiung
Abstract :
In this paper, a hybrid supervised learning algorithm for neural network was proposed. The problem of local minimum learning usually occurred in the real application of neural network is tried to be solved or reduced. In order to improve the efficiency and stability of conventional error back-propagation learning algorithm, a hybrid learning method combining the linear multi-regression and backpropagation techniques was developed. To demonstrate the superiority of the method we developed, one example was simulated. The conventional BP learning method was also performed as the comparison with the new method proposed. From the results shown, the conventional BP method easily makes neural model plunge into the local minima. On the contrary, the new method we proposed not only has a fast learning, but also has a better learning efficiency.
Keywords :
backpropagation; neural nets; regression analysis; error back-propagation learning algorithm; hybrid supervised neural network learning algorithm; linear multiregression technique; local minimum learning; Backpropagation algorithms; Convergence; Information management; Iterative algorithms; Learning systems; Neural networks; Stability; Supervised learning; System identification; Transfer functions;
Conference_Titel :
Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on
Conference_Location :
Dalian, Liaoning
Print_ISBN :
978-0-7695-3161-8
Electronic_ISBN :
978-0-7695-3161-8
DOI :
10.1109/ICICIC.2008.607