摘要
为客观、合理地进行气膜薄壳钢筋混凝土穹顶储仓的工期预测,提出了基于PSO-SVR的预测方法。采用粒子群算法(particle swarm optimization,PSO)对支持向量回归机(support vector regression,SVR)的参数进行优化,并运用优化后的支持向量回归机对气膜薄壳钢筋混凝土穹顶储仓的工期进行预测。通过实例验证表明:PSO-SVR模型的预测效果优于遗传算法(GA-SVR)和串联型灰色神经网络(SGNN)。
In order to forecast the duration on thin-shell concrete dome using inflated forms objectively and reasonably, the artical presents a prediction method named PSO-SVR. Using PSO to optimize the parameter of SVR, and forecasting the duration on thin-shell concrete dome using inflated forms by support vector regression which is optimized. The example show that the prediction effect of PSO-SVR model is better than genetic algorithm（GA-SVR） and series of grey neural network（SGNN）.
出处
《价值工程》
2017年第5期35-37,共3页
Value Engineering
