为了提高相符预测器的计算效率,在算法中引入基于核的度量学习.将其学习过程分解成2部分:先通过提高75%的训练样本的类可分性获得1个优化核;然后在优化的核空间中采用k近邻方法设计奇异度函数,并使用剩下的25%的样本实现标准的相符预测器算法.将新算法应用于田纳西-伊斯曼过程的多类故障诊断问题,实验结果表明,在保证高的预测效率的同时,新算法可以显著降低计算时间.In order to improve the computational efficiency of conformal predictora,procedure of adaptive kernel-based distance metric learning was incorporated in the algorithm.The learning process was divided into two stages.Firstlya,n op-timized kernel was obtained by increasing the class separability of 75% of the training samples.Secondlyt,he k nearest neighbor classifier was used to design a nonconformity measure function in the optimized kernel space.And then the stan-dard conformal predictor algorithm was conducted on the remaining 25% of the training samples.The new method was ap-plied to the multiple fault diagnosis of Tennessee Eastman process.The results show that the new algorithm provides substan-tial reductions in computational timea,nd ensures high predictive efficiency as well.厦门大学985二期工程信息创新平台资助项目(0000-x07204);厦门市科技计划资助项目(3502Z20083028
