基于SVM和PSO算法的飞机部件DMC预计方法  被引量:6

Improved PSO and its combined application with SVM in the DMC estimation of aircraft components

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作  者:吴静敏[1] 左洪福[1] 

机构地区:[1]南京航空航天大学民航学院,江苏南京210016

出  处:《系统工程与电子技术》2007年第1期151-154,共4页Systems Engineering and Electronics

摘  要:控制维修成本是飞机研制中的一项重要任务,而部件直接维修成本(DMC)的预计是控制过程中的关键步骤。鉴于现有的预计方法精度不高、波动性大或可操作性不强,引入了支持向量机(SVM)理论对不同单一模型进行非线性组合,并改进了粒子群优化(PSO)算法用于同时求解离散变量和连续变量,达到了模型选择和SVM参数的联合优化。实验证明,该预计方法算法简单、速度快,并且比以往的方法在精度和稳定性上都有显著提高。Controlling maintenance cost is an important task during the aircraft development stage, and estimating the direct maintenance cost (DMC) of the component is a pivotal step in the control processes. As existing estimation methods are always low accuracy, great fluctuation or bad operability, a new method based on support vector machine (SVM) theory and particle swarm optimization (PSO) algorithm is presented in this paper. The SVM theory is used to combine different individual models, the PSO algorithm is improved to optimize discrete variables and continuous variables in the same framework and used to choose models and optimize parameters for SVM. Experiments show that this method is simple and quick. And it is obviously more accurate and stable than the former ones.

关 键 词:维修成本 粒子群优化 支持向量机 组合预测 

分 类 号:TP18[自动化与计算机技术—控制理论与控制工程] V267[自动化与计算机技术—控制科学与工程]

 

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