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基于神经网络-遗传算法优化柴油机油台架试验

Optimization for Engine Test of Engine Oil Based on Genetic Algorithm-neural Network

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【Author】 Li Weimin~(1,2) Xu Chunming~1 Chi HuiWai~3(1.State Key Laboratory of Heavy Oil Processing,China University of Petroleum,Beijing 102200,China;2.Department of Chemical Engineering,Jiangsu Polytechnic University,Changzhou Jiangsu 213016,China;3.Department of Chemical Engineering,Hong Kong University of Science and Technology,Hong Kong,China)

【机构】中国石油大学(北京)重质油国家重点实验室；香港科技大学化工系北京102200；江苏工业学院化工系江苏常州213016；北京102200；香港；

【摘要】根据15W/40 CD柴油机油的模拟实验与台架试验的基础数据,用人工神经网络(ANN)的反向传播算法建立了模拟实验与台架试验神经网络预测模型,该神经网络模型合适的拓扑结构为5-7-1,学习速率为0.2,动量因子为0.9。探讨了用模拟实验数据预测台架试验结果的可能性,检验证明用人工神经网络方法建立的模型能准确预报15W/40CD柴油机油的台架试验结果。该神经网络预测模型用遗传算法优化,得到了15W/40 CD级柴油机油能通过台架试验的最优模拟实验结果。更多还原

【Abstract】 Based on the data of bench test and engine test of 15W/40 CD grade engine oil,an artificial neural network(ANN) model was developed for predicting the relationship between engine test and bench test using back-propagation algorithm.The appropriate topology of ANN was 5-7-1.The learning rate of ANN was 0.2,and the momentum factor of ANN was 0.3.It is shown that the ANN model can correlate and predict results of engine test of 15W/40 CD grade engine oil with much accuracy.The ANN model was optimized by incorporating genetic algorithm.Optimal bench test of 15W/40 CD grade engine oil was obtained using the ANN model developed.更多还原

【关键词】柴油机油；模拟实验；台架试验；神经网络；遗传算法；
【Key words】 diesel engine oil； bench test； engine test； artificial neural network； genetic algorithm；

【基金】国家重点基础研究发展规划项目(G20000263)

【文献出处】润滑与密封 ,Lubrication Engineering , 编辑部邮箱 ,2006年04期

【分类号】TK427
【被引频次】3
【下载频次】174

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