摘要
文章提出一种模拟退火(SA)与粒子群优化(PSO)算法相结合的算法来优化Elman神经网络权值和阈值。当PSO处于停滞状态时,利用粒子群优化算法的全局寻优性质,以及SA能跳出局部最优解的特性,在搜索到的最优位置处用模拟退火算法继续寻找最优解,并对具有动态递归性能的Elman神经网络进行学习训练,这样就能对忙时话务量进行预测。结果表明,与传统Elman神经网络和PSO-Elman神经网络相比,基于模拟退火粒子群算法训练的神经网络具有更高的预测精度和良好的自适应性。
This paper presents a hybrid algorithm that combines simulated annealing （SA） algorithm with parti-cle swarm optimization （PSO） algorithm to optimize the weights and threshold of Elman neural network. By using the advantages of global optimization of PSO, when it is trapped into local optimum, SA is employed to jump out of local optimal solution to find the global optimal solution. The hybrid algorithm is used to train Elman neural network with dynamic recursive properties. The approach is carried out on the forecasting of the busy telephone traffic. The experimental results show that SAPSO-Elman neural network has better precision and adaptability compared with the traditional neural network.
出处
《激光杂志》
CAS
CSCD
北大核心
2014年第7期36-38,42,共4页
Laser Journal
基金
中国移动通信集团新疆有限公司研究发展基金项目(项目编号:XJM2013-01)
关键词
模拟退火
粒子群算法
ELMAN神经网络
话务量预测
Simulated annealing
Particle swarm optimization
Elman neural network
Traffic prediction
