【摘要】 为解决BP神经网络模式识别时训练准则和分类准则不一致的问题,采用可拓学的扩缩变换,通过在输出空间中用一个特定区域(称作教师区域)来代替教师信号,极大地提高了可拓神经网络模型的训练速度,同时彻底解决网络训练和实际分类准则不一致的问题．计算实例表明,可拓神经网络是一个有效的模式识别工具．更多还原

【Abstract】 At present,there is a problem that the training rule and classifying rule is not consistent when the mostly used BP neural network is applied in pattern recognition.The core content of Extenics is extension transform,that is,to change unchangeable things to changeable ones,or to change“no”to“yes”,and to change opposite things to union things.By extension transform,using a region in the outputting space replaces a point,the training speed is extraordinarily improved and the problem of inconsistent training rule and classif- ying rule is solved.It proves that extension neural network is valid for pattern recognition.更多还原