摘要
针对时变信号动态模式分类问题,提出一种过程支持向量机模型PSVM.过程支持向量机的输入可为时变函数(或函数向量),通过核函数变换将动态模式映射到高维特征空间,经过对训练样本集中过程支持向量类别特性的学习,可直接建立时变信号的分类模型.给出过程支持向量机的一般模型和求解算法,油水层判别仿真实验结果验证了模型和算法的有效性.
Aiming at the problem of classification of Time-Varying Signal patterns, this paper presents a Process Support Vector Machine (PSVM) model. The input of PSVM can be functions with time-varying (or function vector) according to kernel function transforming, map dynamic pattern to high-dimensional feature space. For the study of PSVM classification in training sample set, it can build classification model of time-varying signals directly. The model of PSVM and its algorithm for solving were given. The results of simulation experiments confirmed the efficiency of the model and algorithm.
出处
《大庆石油学院学报》
CAS
北大核心
2008年第4期111-113,共3页
Journal of Daqing Petroleum Institute
基金
黑龙江省自然科学基金(ZA2006-11)
黑龙江省科技攻关项目(GZ07A103)
关键词
过程支持向量机
时变信号
动态模式分类
求解算法
应用
process support vector machine
time-varying signal
dynamic pattern classification
solving algorithm
application