基于支持向量机的气体传感阵列模式识别方法研究
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摘要
随着科学技术的进步和工业生产的发展,对气体的检测不仅要求快速、准确,而且还要求检测能够在线进行。由于气体传感器普遍存在交叉敏感,单一气体传感器无法对多种气体进行准确的定性识别和定量检测。因此,将多个气体传感器组成阵列与模式识别方法相结合进行多组分气体的识别与检测具有更大的实用价值。而模式识别技术对识别效果起着关键性的作用。
本文以电力变压器油中六种溶解气体(H2,CO,CH4,C2H4,C2H2,C2H6)在线检测为例,提出了一种基于免疫加权支持向量回归机的气体传感阵列模式识别方法。论文依次讲述了气体传感阵列国内外研究现状和常用模式识别方法,并对MQ型气体传感器进行特性实验研究,介绍了支持向量机模式识别理论,最后着重对基于支持向量机的气体传感阵列模式识别方法进行了逐步深入的研究,在利用气体传感阵列获取气体组分和浓度多维信息的基础上,结合支持向量机模式识别理论,对变压器油中溶解气体进行定量分析。
首先对MQ型气体传感器进行了特性实验,通过实验验证了MQ系列气体传感器具有良好的灵敏度、重复性和响应特性,但同时存在一定的交叉敏感。针对神经网络模式识别方法存在网络结构难于确定、易陷入局部极小值等问题,分别采用BP神经网络和标准支持向量回归机(SVR)对气体传感阵列测得的信号进行模式识别。结果表明,经过SVR进行模式识别后的值明显优于BP神经网络识别值。将免疫算法用于支持向量机参数优化过程中,利用免疫算法保持群体多样性的特点进行参数全局寻优,将参数优化后的支持向量机应用于气体传感阵列模式识别中。通过对比表明,免疫支持向量机克服了标准支持向量机(SVM)采用试凑法确定参数的不足,识别精度较SVM有进一步的提高。
针对标准支持向量回归机中未考虑各样本重要性的差异问题,给各个样本的惩罚系数赋予不同权重,分别采用线性插值法和非线性插值法对参数进行加权,将经过免疫算法全局寻优和参数加权后的支持向量机应用于气体传感阵列模式识别中。结果表明,免疫加权支持向量机具有更高精度的识别效果,更好的性能和应用前景。
As the advancement of science and technology and the development of industry, the detection of gases requires not only fast, accurate, but also testing on-line. Caused by the cross-sensitivity of the gas sensors, it is impossible for a single gas sensor to identify multiple gases. So, identification of multi-component gas based on gas sensor array and pattern recognition is becoming an important way in dealing with cross-sensitivity in gas analysis. The pattern recognition technology plays the crucial role to the recognition effect.
Taking the detection of six kinds of transformer oil dissolved gas (H2, CO, CH4, C2H4, C2H2, C2H6) as an example, this article proposed a pattern recognition method of gas sensor array based on immune weighted support vector machine for regression. The current research situation and commonly used pattern recognition methods are described at the beginning of the thesis. Characteristic experiments of MQ type gas sensors are carried. The author introduced the theory of support vector machine and pattern recognition method of gas sensor array based on SVM is studied step by step. The theory of SVM is used to do quantitative analysis after obtaining the multi-dimensional information of the gas composition and concentration by gas sensor array.
Firstly, characteristic experiments of MQ type gas sensors are carried. Research shows that these gas sensors have good performance on sensitivity, repeatability and response characteristic. But cross sensitivity also exists at the same time.
Secondly, aiming at the problems such as difficult determination of net structure and local minimization of neural networks, the BP neural networks and the standard SVR are used in the gas sensor array signals pattern recognition. The result shows the values of standard SVR is better than BP neural networks. Immune algorithm is used in the SVM parameters optimization process. Because of immune algorithm can maintain the diversity of the group, the parameters can be adjusted globally and use it in sensor array signals pattern recognition. Results show that, this kind of pattern recognition method overcomes the shortages of the trial-and-error method, and improves recognition accuracy of SVM.
Finally, aiming at the problem of no considering the importance of each sample in the standard SVR, each training sample is assigned different approximation error requirement and different penalty. the thesis take linear interpolation method and nonlinear interpolation method to the weighted parameters and use the SVM which combine immune algorithm with parameters weighted in the gas sensor array signals pattern recognition. The results show that, immune weighted support vector machine have better recognition accuracy, performance and application prospect.
本文以电力变压器油中六种溶解气体(H2,CO,CH4,C2H4,C2H2,C2H6)在线检测为例,提出了一种基于免疫加权支持向量回归机的气体传感阵列模式识别方法。论文依次讲述了气体传感阵列国内外研究现状和常用模式识别方法,并对MQ型气体传感器进行特性实验研究,介绍了支持向量机模式识别理论,最后着重对基于支持向量机的气体传感阵列模式识别方法进行了逐步深入的研究,在利用气体传感阵列获取气体组分和浓度多维信息的基础上,结合支持向量机模式识别理论,对变压器油中溶解气体进行定量分析。
首先对MQ型气体传感器进行了特性实验,通过实验验证了MQ系列气体传感器具有良好的灵敏度、重复性和响应特性,但同时存在一定的交叉敏感。针对神经网络模式识别方法存在网络结构难于确定、易陷入局部极小值等问题,分别采用BP神经网络和标准支持向量回归机(SVR)对气体传感阵列测得的信号进行模式识别。结果表明,经过SVR进行模式识别后的值明显优于BP神经网络识别值。将免疫算法用于支持向量机参数优化过程中,利用免疫算法保持群体多样性的特点进行参数全局寻优,将参数优化后的支持向量机应用于气体传感阵列模式识别中。通过对比表明,免疫支持向量机克服了标准支持向量机(SVM)采用试凑法确定参数的不足,识别精度较SVM有进一步的提高。
针对标准支持向量回归机中未考虑各样本重要性的差异问题,给各个样本的惩罚系数赋予不同权重,分别采用线性插值法和非线性插值法对参数进行加权,将经过免疫算法全局寻优和参数加权后的支持向量机应用于气体传感阵列模式识别中。结果表明,免疫加权支持向量机具有更高精度的识别效果,更好的性能和应用前景。
As the advancement of science and technology and the development of industry, the detection of gases requires not only fast, accurate, but also testing on-line. Caused by the cross-sensitivity of the gas sensors, it is impossible for a single gas sensor to identify multiple gases. So, identification of multi-component gas based on gas sensor array and pattern recognition is becoming an important way in dealing with cross-sensitivity in gas analysis. The pattern recognition technology plays the crucial role to the recognition effect.
Taking the detection of six kinds of transformer oil dissolved gas (H2, CO, CH4, C2H4, C2H2, C2H6) as an example, this article proposed a pattern recognition method of gas sensor array based on immune weighted support vector machine for regression. The current research situation and commonly used pattern recognition methods are described at the beginning of the thesis. Characteristic experiments of MQ type gas sensors are carried. The author introduced the theory of support vector machine and pattern recognition method of gas sensor array based on SVM is studied step by step. The theory of SVM is used to do quantitative analysis after obtaining the multi-dimensional information of the gas composition and concentration by gas sensor array.
Firstly, characteristic experiments of MQ type gas sensors are carried. Research shows that these gas sensors have good performance on sensitivity, repeatability and response characteristic. But cross sensitivity also exists at the same time.
Secondly, aiming at the problems such as difficult determination of net structure and local minimization of neural networks, the BP neural networks and the standard SVR are used in the gas sensor array signals pattern recognition. The result shows the values of standard SVR is better than BP neural networks. Immune algorithm is used in the SVM parameters optimization process. Because of immune algorithm can maintain the diversity of the group, the parameters can be adjusted globally and use it in sensor array signals pattern recognition. Results show that, this kind of pattern recognition method overcomes the shortages of the trial-and-error method, and improves recognition accuracy of SVM.
Finally, aiming at the problem of no considering the importance of each sample in the standard SVR, each training sample is assigned different approximation error requirement and different penalty. the thesis take linear interpolation method and nonlinear interpolation method to the weighted parameters and use the SVM which combine immune algorithm with parameters weighted in the gas sensor array signals pattern recognition. The results show that, immune weighted support vector machine have better recognition accuracy, performance and application prospect.
引文
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[3]刘解放,侯振雨,吴亮.支持向量机在模式识别和回归模型中的应用[J].河南科技学院学报(自然科学版) , 2007,(04).
[4]王耀龙.基于油中溶解气体分析的电力变压器故障诊断[D].广西大学, 2005.
[5]孙毓润.大型变压器油色谱在线监测装置[M].东北电力科学研究院, 1994.
[6]杨高锋,电子鼻系统在电气火灾检测中的应用[D].浙江大学, 2006.
[7]王鹏,人工嗅觉系统检测室内污染混合气体的研究.浙江大学, 2003.
[8] Emmanuel Scorsone, Anna Maria Pisanelli, Krishna C. Persaud. Development of an electronic nose for fire detection[J]. Sensors and Actuators B, 2006, 116:55-58.
[9] Jacques Nicolas, Anne-Claude Romain, Catherine Ledent. The electronic nose as a warning device of the odour emergence in a compost hall[J]. Sensors and Actuators B, 2006, 116:95-99.
[10]王晗,基于动态特征的便携式电子鼻系统的研究与实现[D].西北工业大学, 2005.
[11]张覃轶,电子鼻:传感器阵列、系统及应用研究[D].华中科技大学, 2005.
[12]栗淑利,基于人工神经网络的酒精识别电子鼻研究[D].大连理工大学, 2005.
[13]庞林江,电子鼻技术在小麦陈化评定中的应用研究[D].浙江大学, 2005.
[14] Qinyi Zhang, Slumping Zhang, Changsheng Xie, et al. Characterization of Chinese vinegars by electronic nose[J]. Sensors and Actuators B, 2006, 119:538-546.
[15] Maria S.Cosio, Davide Ballabio, Simona Benedetti, et al. Geographical origin and authentication of extra virgin olive oils by an electronic nose in combination with artificial neural networks[J]. Analytica Chimica Acta, 2006, 567:201-210.
[16] Roberto Paolesse, Adriano Alimelli, Eugenio Martinelli, et al. Detection of fungal contamination of cereal grain samples by an electronic nose[J]. Sensors and Actuators B, 2006, 119:425-430.
[17] Gomez, A H, Wang Jun, Hu GuiXian, et al. Discrimination of storage shelf-life for mandarin by electronic nose technique[J]. LWT, 2007, 40:681-689.
[18] Lakshmi P. Pathange, et al. Non-destructive evaluation of apple maturity using an electronic nose system. Journal of Food Engineering, 2006, 77:1018-1023.
[19]徐良,新型气体传感器及呼吸诊断电子鼻设计[D].浙江大学, 2003.
[20]曹明富,肺癌呼吸气体检测电子鼻及诊断方法的研究[D].浙江大学, 2006.
[21] Roberto F. Machado, Daniel Laskowski, Olivia Deffenderfer, Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath[J]. American Journal of Respiratory and Critical Care Medicine, 2005, 171:1286-1291.
[22] Ritaban Dutta, Aruneema Das, Nigel G. Stocks, et al. Stochastic resonance-based electronic nose: A novel way to classify bacteria[J]. Sensors and Actuators B, 2006, 115:17-27.
[23]李绪,嗅觉神经系统网络模型的研究和应用[D].浙江大学, 2004.
[24]工米娜,电子鼻动态特征信息处理方法研究[D].西北工业大学, 2005.
[25]王岩,基于独立分量分析与神经网络的电子鼻模式识别[D].西南交通大学, 2006.
[26] Poprawski J, Boilot P, Tetelin F, Counterfeiting and quantitication using an electronic nose in the perfumed cleaner industry[J]. Sensors and Actuators B, 2006,116:156-160.
[27] Sobanski T, Szczurek A, Nitsch K, Electronic nose applied to automotive fuel qualification [J]. Sensors and Actuators B, 2006, 116:207-212.
[28]聂伟,王祁,孙圣和.基于模糊集合理论数据融合的气体识别方法研究[J].传感技术学报, 1999, 6:127-133.
[29] Nello Cristianini, John Shawe-Taylo, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods[M].北京:机械工业出版社, 2005.
[30]张国云,王炼红,谭建豪.基于支持向量机的智能嗅敏技术研究.变压器.2008,45:30-32
[31] Haugen J E,Kvaal K. Electronic nose and artificial neural network. Meat Sci,1998,49: 273-286.
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