CAJ | 학술논문

特征选择在BP神经网络算法中起着重要作用，顺序前向选择算法（SFS算法）利用前向搜索叠加的方式，从众多的原始特征中获得对分类识别算法最有效的主要特征，实现样本特征维数压缩。提出一种改进SFS特征选择算法，设计了加权判别函数和测试反馈停止准则。实验证明，改进算法能有效压缩样本特征维数，提高BP网络收敛速度和正确识别率。
특정선택재BP신경망락산법중기착중요작용，순서전향선택산법（SFS산법）이용전향수색첩가적방식，종음다적원시특정중획득대분류식별산법최유효적주요특정，실현양본특정유수압축。제출일충개진SFS특정선택산법，설계료가권판별함수화측시반궤정지준칙。실험증명，개진산법능유효압축양본특정유수，제고BP망락수렴속도화정학식별솔。
Feature selection plays an important role in the BP neural network algorithm. Sequence forward selection(SFS) algorithm can realize the compression of sample feature dimension by using a way of forward search superimposition to get the most efficient main feature of classification recognition algorithm from numerous original features. An improved SFS feature selec?tion algorithm is proposed in this paper. Weighted discriminant function was designed and feedback stopping criterion was tested. The experimental results show that the improved algorithm can effectively compress the sample feature dimension,as well as im?prove BP network astringency and correct recognition rate.