针对电子鼻应用中传感器漂移现象导致的电子鼻分类准确率降低问题，文中提 出一种基于样本分布加权的跨域极限学习机模型。该模型考虑到单个样本对全局分布差 异度量的贡献度不同，以基于样本分布加权的最大均值差异作为衡量领域间样本分布差 异的度量，将源域和目标域数据投影到高维的极限学习机特征空间中，然后寻找一个合 适的投影方向，将特征空间中的数据映射到一个公共子空间中，使得子空间中源域数据 和目标域数据具有相似的分布。使用 Matlab 对该算法进行仿真，并对比不同的隐含层 节点数对该算法识别率的影响，以验证该算法的可行性。结果表明，文中提出的算法模 型可以明显减小两个域间数据的分布差异，满足传统的分类学习算法对训练和测试数据 的分布要求，从而提高电子鼻的分类准确率。

To solve the problem of low classification accuracy of electronic nose ( E-nose) caused by sensor drift in various applications，a sample distribution weighting cross domain extreme learning machine model was proposed． Considering the different contributions of a single sample to the global distribution discrepancy measure，it uses the maximum mean discrepancy based on the sample distribution weighting as a measure of the sample distribution discrepancy between domains． The data of source domain and target domain were projected onto a high-dimensional extreme learning machine feature space． Then a suitable projection direction was found，by which the data was projected onto a common subspace． Thus the source domain and target domain data in the subspace had a similar distribution． Matlab was used to simulate this algorithm，and the effects of different number of hidden layer nodes on the recognition rate of the algorithm were compared so as to verify the feasibility of the algorithm． The results show that the model proposed in this paper can significantly reduce the distribution discrepancies between the two domains，and can meet the distribution requirements of traditional classification algorithms for training and test data，thus to improve the classification accuracy of E-nose．