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SVM和BP算法在气体识别中的对比研究 被引量:9

Research of Gas Classification Based on SVM Compared with BP
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摘要 介绍了一种可以应用于气体识别领域的新的算法 -支持向量基算法 (SVM) ,并通过同常规的神经网络算法-BP算法进行实验对比 ,得到了 :SVM算法在数据样本不含噪声时可以得到和BP算法同样好的识别效果 ;在数据言本含有噪声时 ,该算法的识别效果相对BP算法具有明显的优势。从而证明了SVM算法在气体识别领域具有良好的研究价值和应用前景。 A new algorithm will be introduced which has not been paid enough attention to Support Vector Machines (SVM). Contrasting with BP algorithm which is very normal in the field of Neural Network, some useful conclusion can be gained during experiments as following: By using SVM, we can get the same impact as BP algorithm when the data do not have noises. But when the data has noises, using SVM will get better effect than using BP. The conclusion shows that SVM algorithm has well research value and applied foreground in the area of gas classification.
作者 汪丹 张亚非
出处 《传感技术学报》 CAS CSCD 北大核心 2005年第1期201-204,共4页 Chinese Journal of Sensors and Actuators
关键词 支持向量机 气体传感器 神经网络 气体识别 BP算法 SVM gas sensor neural network gas classification BP
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  • 1蔡煜东,姚林声.传感器非线性校正的人工神经网络方法[J].仪器仪表学报,1994,15(3):299-302. 被引量:21
  • 2沈清.神经网络应用技术[M].长沙:国防科技大学出版社,1993..
  • 3康耀红.数据融合技术与应用[M].西安:西安电子科技大学出版社,1997..
  • 4VAPNIK V N. The nature of statistical learning [M].Berlin:Springer, 1995.
  • 5VAPNIK V N. Statistical learning theory [M]. New York:John Wiley & Sons, 1998.
  • 6SCHōLKOPH B, SMOLA A J, BARTLETT P L. New support vector algorithms[J]. Neural Computation.2000, 12(5):1207--1245.
  • 7SUYKENS J A K, VANDEWALE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293--300.
  • 8CHEW H-G, BOGNER R E, LIM C-C, Dual v-support vector machine with error rate and training size beasing[A]. Proceedings of 2001 IEEE Int Conf on Acoustics,Speech, and Signal Processing [C]. Salt Lake City,USA: IEEE, 2001. 1269--1272.
  • 9LIN C-F, WANG S-D. Fuzzy support vector machines[J]. IEEE Trans on Neural Networks, 2002, 13(2):464--471.
  • 10SUYKENS J A K, BRANBANTER J D, LUKAS L, et al. Weighted least squares support vector machines:robustness and spare approximation[J]. Neuroeomputing, 2002, 48(1): 85--105.

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