改进粒子群优化神经网络及其在产品质量建模中的应用

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.

Improved particle swarm optimized back propagation neural network and its application to production quality modeling

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 effectively help the BPNN to get a better regression precision and prediction performance.