摘要
参数选择是支持向量机(SVM)研究领域的重要问题,它的本质是一个优化搜索过程,考虑到进化算法在求解优化问题上的有效性,提出了以最小化k-fold交叉验证误差为目标．粒子群优化(PSO)算法为寻优技巧的SVM参数调整方法．通过仿真例子验证该方法的有效性后,用其建立了聚丙烯腈生产过程中数均分子量的软测量模型,结果表明该方法有效．
Parameters selection is an important problem in the research area of support vector machines （SVM）, and its nature is an optimization problem. Motivated by the effectiveness of evolution algorithm on optimization problem, a new automatic searching methodology, based on particle swarm optimization （PSO） algorithm, is proposed in this paper. Each particle indicates a group of SVM parameters, and the population is a collection of particles in this method. Furthermore, the k-fold cross-validation error is used as the fitness function of PSO. After having been validated its effectiveness by two artificial data experiments, the proposed method is then applied to establish a soft-sensor model for average molecular weight in polyacrylonitrile productive process. Finally, real data simulation results are also given to show the efficiency.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2006年第5期740-743,748,共5页
Control Theory & Applications
基金
国家高技术研究发展计划(863计划)资助项目(2002AA412120).
关键词
支持向量机
参数选择
粒子群优化
聚丙烯腈
软测量
support vector machines
parameters selection
particle swarm optimization
polyacrylonitrile
soft-sensor
