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  • Title: [Method of infrared spectrum on-line pattern recognition of mixed gas distribution based on SVM].
    Author: Bai P, Ji JZ, Zhang FQ, Li Y, Liu JH, Zhu CC.
    Journal: Guang Pu Xue Yu Guang Pu Fen Xi; 2008 Oct; 28(10):2278-81. PubMed ID: 19123388.
    Abstract:
    In order to solve the difficulties that the spectrum training data samples of the massive mixed gas cannot be actually obtained, the analysis precision is low and it is not real time online analysis in the analysis of mixed gas component concentration, the support vector machine, a new information processing method, was used in the mixed gas infrared spectrum analysis, and the concept of mixed gas distribution pattern was proposed in the present paper. Based on the thought that the mixed gas distribution pattern recognition is carried out first, and then the analysis work of mixed gas component concentration is done, sixty kinds of mixed gas distribution pattern were determined after investigation and study, and 6000 mixed gas spectrum data samples were used for model training and testing. Sequential minimal optimization algorithm was applied to realize the decrement and the increase of online learning, and finally the model of infrared spectrum online pattern recognition of mixed gas distribution based on SVM was established. The model structure is composed of 2 levels, pattern recognition level and result output level. The pattern recognition level completes the task of mixed gas distribution pattern recognition; while the result output level is composed of 60 SVM calibration models, and it completes the task of mixed gas concentration analysis. Experimental results show that the correct recognition rate of mixture gas distribution pattern is not lower than 98.8%, and that the method can be used for online recognition of mixed gas distribution pattern under the conditions of small samples of a mixed gas, and can add new mixed gas online, and it has the practical application value.
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