Functional Link Neural Network Prediction on Composite Regeneration Time of Diesel Particulate Filter for Vehicle Based on Fuzzy Adaptive Variable Weight Algorithm
Functional Link Neural Network Prediction on Composite Regeneration Time of Diesel Particulate Filter for Vehicle Based on Fuzzy Adaptive Variable Weight Algorithm
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DOI：
10.12733/jics20103209
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发表时间：
2014-04
期刊：
影响因子：
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通讯作者：
Qing-song Zuo;E. Jiaqiang;J. Gong;Lijun Li;Tao Chen;Shu-hui Wang;Guohai Jia
中科院分区：
文献类型：
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作者：
Qing-song Zuo;E. Jiaqiang;J. Gong;Lijun Li;Tao Chen;Shu-hui Wang;Guohai Jia
为提高车用柴油机微粒捕集器复合再生时间的预测精度，选取各预测模型的不同预测值作为函数连接神经网络的原始输入值，在满足泛函拟合的充要条件后，建立了基于模糊自适应变权算法的复合材料再生时间泛函连接神经网络预测模型链接神经网络进行了分析。模型的应用结果表明，|Emax|基于模糊自适应变权算法的车用柴油机微粒捕集器复合再生时间函数链神经网络预测模型的最大相对误差小于0.86%，表明该预测模型具有较高的精度。结果表明，影响车用柴油机微粒捕集器复合再生时间的因素由大到小依次为：排气氧浓度、排气质量流量、微波功率、排气温度和铈基添加剂的添加量。
In order to enhance the precision of prediction on composite regeneration time of diesel particulate filter for vehicle, different predictive values of each prediction model are selected as the primeval input values of functional link neural network, and the functional link neural network prediction model of composite regeneration time based on fuzzy adaptive variable weight algorithm is established after the necessary and sufficient conditions for fitting of functional link neural network are analyzed. The application result of the model shows that the absolute value |emax| of maximum relative error of functional link neural network prediction model on composite regeneration time of diesel particulate filter for vehicle based on fuzzy adaptive variable weight algorithm is less than 0.86%, indicating the high accuracy of the prediction model. Moreover, the result draws that the factors influencing composite regeneration time prediction of diesel particulate filter for vehicle, influence degree of which is from big to small, are exhaust oxygen concentration, exhaust mass flow, microwave power, exhaust temperature and the amount of cerium-based additive.