Study on the Quantitative Detection for Gas Mixtures by the Improved Genetic Algorithm and BP Neural Network
Based on the advantages and disadvantages of genetic algorithm (GA) and artificial neural network (ANN),an optimization model with the adaptive genetic algorithm and the traditional BP neural network is presented for the quantitative detection of gas mixtures. To overcome the disadvantages of ANN with inherent slowly searching rate and partially leading to minimum,the adaptive genetic algorithm is used to get better initial weights and thresholds of the BP network in the early stage,which combines the advantages of genetic algorithm with parallelcomputing and strong whole searching capacity. In the later,the network is trained by the error back propagation method.A three-layer 7×l8×3 BP network is designed for a group of gas mixtures with five samples. The results show that the convergence speed and the learn precision of adaptive genetic algorithm optimizing neural network are better than that of the traditional BP algorithm,which can make shorter the calculation time three times at the begin of the same weights and thresholds and at the end of global error with the magnitude of l×104.The application of GA optimizing BP network to the recognition of gas mixtures is reliable and the method can improve the detection efficiency of gas mixtures,which can give some references for developing intelligent detection apparatus.
Gas detection Adaptive genetic algorithm Error back propagation algorithm
Yang xianjiang Yuan Lizhe Wang Yu
Nanjing Artillery Academy,Langfang China
国际会议
西安
英文
2371-2374
2011-12-23（万方平台首次上网日期，不代表论文的发表时间）