Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction
Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction
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DOI：
10.1177/1468087411420280
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发表时间：
2012-02
影响因子：
2.5
通讯作者：
P. Wong;Hang-Cheong Wong;C. Vong
中科院分区：
文献类型：
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作者：
P. Wong;Hang-Cheong Wong;C. Vong
在所有发动机控制变量中，燃料效率和污染减少与空气比（即λ）控制密切相关。λ表示实际可用的空燃比混合物与所使用的燃料的化学计量空燃比的差异量。准确的λ预测对于有效的λ控制至关重要。本文采用了一种新兴的在线时间序列增量算法，提出了一种新的在线时间序列递减算法的最小二乘支持向量机（LS-SVM）的基础上不断更新的LS-SVM lambda函数，每当一个样本被添加到，或删除，训练数据集。此外，在线时序算法也可以显着缩短功能更新时间相比，从零开始的功能重新训练。为了评估这对在线时间序列算法的有效性，在不同的操作条件下，从实验中获得的三个lambda时间序列。在线时间序列算法在未知情况下的预测结果进行了比较，在经典的LS-SVM，典型的递减LS-SVM，神经网络。实验结果表明，在线时间序列增量和减量LS-SVM上级其他三种典型方法。
Fuel efficiency and pollution reduction relate closely to air-ratio (i.e. lambda) control among all the engine control variables. Lambda indicates the amount that the actual available air-fuel ratio mixture differs from the stoichiometric air-fuel ratio of the fuel being used. Accurate lambda prediction is essential for effective lambda control. This paper employs an emerging online time-sequence incremental algorithm and proposes one novel online time-sequence decremental algorithm based on least squares support vector machines (LS-SVMs) to continually update the built LS-SVM lambda function whenever a sample is added to, or removed from, the training dataset. Moreover, the online time-sequence algorithm can also significantly shorten the function updating time as compared with function retraining from scratch. In order to evaluate the effectiveness of this pair of online time-sequence algorithms, three lambda time series obtained from experiments under different operating conditions are employed. The prediction results of the online time-sequence algorithms over unseen cases are compared with those under classical LS-SVMs, typical decremental LS-SVMs, and neural networks. Experimental results show that the online time-sequence incremental and decremental LS-SVMs are superior to the other three typical methods.