Title :
A Method of Improved BP Neural Algorithm Based on Simulated Annealing Algorithm
Author :
Bai, Kai ; Xiong, Jing
Author_Institution :
Sch. of Comput. Sci., Yangtze Univ., Jingzhou, China
Abstract :
This paper analyses the BP algorithm in detail, including the number of hidden layer, the amount of neural node and training algorithm. In order to improve the training speed, this paper adopts the automatic and adaptive step to perfect the BP algorithm. In addition, because the traditional BP neural network is easy to trap into local minimum, this paper makes use of the characteristic of simulated annealing algorithm and let it unite with BP algorithm. Because the simulated annealing algorithm can get optimal approximation by searching local, it can help BP algorithm not to trap into local minimum.
Keywords :
backpropagation; neural nets; simulated annealing; BP neural network; neural node; neural training algorithm; simulated annealing; Analytical models; Approximation algorithms; Artificial neural networks; Biological neural networks; Biological system modeling; Computational modeling; Computer science; Computer simulation; Genetics; Simulated annealing; BP Neural Algorithm; neural network; simulated annealing algorithm;
Conference_Titel :
Genetic and Evolutionary Computing, 2009. WGEC '09. 3rd International Conference on
Conference_Location :
Guilin
Print_ISBN :
978-0-7695-3899-0
DOI :
10.1109/WGEC.2009.39
