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基于免疫粒子群算法的滑油屑末支持向量机预测模型设计
引用本文:李本威,张赟,孙涛.基于免疫粒子群算法的滑油屑末支持向量机预测模型设计[J].航空动力学报,2009,24(7):1639-1643.
作者姓名:李本威  张赟  孙涛
作者单位:海军航空工程学院飞行器工程系,烟台,264001
摘    要:将人工免疫理论的克隆选择算法中的抗体克隆、变异和抑制策略引入粒子群优化算法中,提出了一种基于克隆选择的免疫粒子群优化算法,克服了基本粒子群算法易于陷入局部最优解的缺点.针对支持向量机预测模型的参数选择影响其预测精度的问题,引入免疫粒子群优化算法设计了参数自适应优化的航空发动机滑油屑末支持向量机预测模型.仿真结果表明:与传统的交叉验证试算法相比,基于免疫粒子群优化的预测模型实现了参数的自动择优,并且提高了预测精度.

关 键 词:克隆选择  粒子群优化  支持向量回归  预测模型
收稿时间:7/12/2008 9:36:36 AM
修稿时间:5/5/2009 12:22:14 PM

Design of forecasting model for aero-engine lubrication debris support vector machines based on immune-particle swarm optimization algorithm
LI Ben-wei,ZHANG Yun and SUN Tao.Design of forecasting model for aero-engine lubrication debris support vector machines based on immune-particle swarm optimization algorithm[J].Journal of Aerospace Power,2009,24(7):1639-1643.
Authors:LI Ben-wei  ZHANG Yun and SUN Tao
Institution:Department of Airborne Vehicle Engineering;Naval Aeronautical Engineering Institute;Yantai;264001;China
Abstract:An immune particle swarm optimization algorithm(immune-PSO) based on clonal selection was proposed.In the method,the strategy of antibody clone,mutation and limit was introduced into PSO algorithm to avoid trapping in local minimum.To resolve the problem that the choice of parameters influences the forecast precision of support vector machine(SVM) forecasting model,the immune-PSO algorithm was used to design the aero-engine lubrication debris forecasting model based on SVM with self-adaptive optimized param...
Keywords:clonal selection  particle swarm optimization  support vector regression  forecasting model  
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