WANG Qin 1, WANG Xiao-dong 1, WU Jian-de 1, et al. Research on tunnel CO volume fraction of neural network and SVM multi-sensor fusion[J]. 2012, 31(7): 6-9+16.
WANG Qin 1, WANG Xiao-dong 1, WU Jian-de 1, et al. Research on tunnel CO volume fraction of neural network and SVM multi-sensor fusion[J]. 2012, 31(7): 6-9+16. DOI: 10.13873/j.1000-97872012.07.029.
To compare effects of the single integration model fusion
and the different optimal weighting information fusion model effects through the study by information fusion model of BP
RBF neural networks
support vector machine(SVM) integration on the failure CO sensor data fusion in the highway tunnel.The simulation test results show that the effectiveness of integration of three kinds of single-fusion model on tunnel CO volume fraction
among which the single SVM fusion model is the best.Optimal weighted fusion performance is superior to a single fusion model
in which BP and SVM
the optimal weighted fusion has the highest precision. The effect rule of the redundancy of precision of single fusion precision between the model outputs on optimal weighted fusion precision is also analyzed.The rule provides a new way to select single models of the optimal weighted fusion.