CAJ | 학술논문

传统方法对中密度纤维板施胶系统数据进行分类时，需要依据已知类别的数据进行划分，数据分类的范围和效率较低。为解决这一问题，提出一种基于模糊支持向量机的数据分类方法，依据模糊算法对未知类别数据进行划分，通过支持向量机对划分后的数据进行训练，提高了中密度纤维板施胶系统数据分类的效率。通过最终的仿真实验结果表明，本文方法模型能够快速、准确地对中密度纤维板施胶系统中的数据进行分类，取得了令人满意的效果。
전통방법대중밀도섬유판시효계통수거진행분류시，수요의거이지유별적수거진행화분，수거분류적범위화효솔교저。위해결저일문제，제출일충기우모호지지향량궤적수거분류방법，의거모호산법대미지유별수거진행화분，통과지지향량궤대화분후적수거진행훈련，제고료중밀도섬유판시효계통수거분류적효솔。통과최종적방진실험결과표명，본문방법모형능구쾌속、준학지대중밀도섬유판시효계통중적수거진행분류，취득료령인만의적효과。
The traditional method of medium density fiberboard (MDF) glue system data classification, need according to the known categories of data, data classification and the range of low efficiency, this paper proposes a data classification method, based on fuzzy support vector machine (SVM) based on fuzzy algorithm for unknown category data, through the support vector machine (SVM) to partition the data after the training, to enhance the efficiency of medium density fiber-board (MDF) glue system data classification. Through the final simulation experiment results show that the method model can fast and accurate classification data from a medium density fiberboard (MDF) glue system, satisfactory results have been achieved.