国家自然科学基金资助项目(61403053);重庆市科技人才培养计划资助项目(cstc2013kjrc-qnrc40005);重庆市教委科学技术研究项目(KJ1400404)。
针对汽车尾气排放的非线性、时变性问题,提出一种三维谱特征下的汽车尾气评估方法。该方法利用频谱分析的原理对汽车尾气进行时频转换,得到尾气的三维谱特征。这些三维谱特征作为输入被提交给径向基神经网络,在K均值聚类算法的驱动下,径向基神经网络完成训练与测试,实现对三维谱特征的分类,从而评估相应的汽车尾气排放水平。数值实验结果表明,提出的汽车尾气评估方法具有较高的准确性。
We present an assessment method of automobile exhaust using three-dimensional spectrogram features to solve the nonlinear and time-varying problems of automobile exhaust emissions. The method takes advantage of spectral analysis to obtain the three-dimensional spectrogram features of automobile exhaust. These three-dimensional spectrogram features, being considered as the input variables, are fed to radial basis function neural network (RBFNN) adapting the K-means algorithm. After completing the training of RBFNN, the three-dimensional spectrograms, being unseen by the network before, are fed to the well-trained network for testing, which achieves the assessment level of automobile exhaust via the classification of three-dimensional spectrogram features. The numerical experiments indicate that the proposed method has a high accuracy.
罗德超,刘国平,刘俊,向飞,袁泉,张顺星.三维谱特征下的汽车尾气评估方法[J].重庆大学学报,2016,39(1):120-126.
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