摘要
针对传统神经网络优化算法易陷入局部最优值的问题,在标准粒子群算法的基础上,对粒子速度与位置更新策略进行改进,提出一种基于改进粒子群优化算法的BP神经网络建模方法.使用sinc函数、波士顿住房数据及某钢厂带钢热镀锌生产的实际数据进行验证.结果表明,与标准的反向传播神经网络和支持向量机相比,基于改进粒子群优化的神经网络模型可以有效提高预测精度.
In order to solve the difficulties of tendency to local optima in conditional optimization algorithms for back propagation neural network （BPNN）, with improvements in the strategy for updating the particle＇s velocity and location, this paper proposed a new back propagation neural network modeling method based on improved particle swarm optimization. The data from sinc function, Boston housing problem and the real strip hot-dip galvanizing production in an iron and steel corporation were used for verification. The results show that, compared with the standard BPNN and support vector machine algorithms, the proposed method can effective- ly help the BPNN to get a better regression precision and prediction performance.
出处
《北京科技大学学报》
EI
CAS
CSCD
北大核心
2008年第10期1188-1193,共6页
Journal of University of Science and Technology Beijing
基金
北京市自然科学基金资助项目(No.3062012)
关键词
BP神经网络
粒子群优化算法
产品质量模型
带钢热镀锌
BP neural network
particle swarm optimization
production quality modeling
strip hot-dip galvanizing
