| 基于人工神经网络烟度计有效光路的算法 |
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| 引用本文： | 杨礼河,孙玉德,解世文,李志军. 基于人工神经网络烟度计有效光路的算法[J]. 天津大学学报(自然科学与工程技术版), 2006, 39(9): 1072-1076 |
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| 作者姓名： | 杨礼河 孙玉德 解世文 李志军 |
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| 作者单位： | [1]天津大学内燃机研究所,天津300072 [2]天津大学机械工程学院,天津300072 |
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| 摘 要： | 为准确计算烟度计的有效光路，基于人工神经网络提出了对QN-1型全流消光式烟度计有效光路的计算方法．该方法利用烟度计有效光路的关键影响因素即空气扫气压力和发动机的转速为输入，构造了一种反向传播神经网络，并利用试验数据进行训练，然后仿真烟度计的有效光路．试验表明，仿真数据与试验数据偏差在-0．0399～0．0486之间．由此可见，利用人工神经网络计算烟度计的有效光路准确实用．
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| 关 键 词： | 人工神经网络 烟度计 有效光路 扫气 压力 算法 |
| 文章编号： | 0493-2137（2006）09-1072-05 |
| 收稿时间： | 2005-09-19 |
| 修稿时间： | 2005-09-192006-04-10 |
Algorithm for the Effective Optical Path of Smoke Meter Based on Artificial Neural Network |
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| YANG Li-he,SUN Yu-de,XIE Shi-wen,LI Zhi-jun. Algorithm for the Effective Optical Path of Smoke Meter Based on Artificial Neural Network[J]. Journal of Tianjin University(Science and Technology), 2006, 39(9): 1072-1076 |
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| Authors: | YANG Li-he SUN Yu-de XIE Shi-wen LI Zhi-jun |
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| Affiliation: | 1. Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072; 2. School of Mechanical Engineering, Tianjin University, Tianjin 300072, China |
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| Abstract: | To calculate the effective optical path of smoke meter accurately, an algorithm for the effective optical path of QN-1 total flow extinction type smoke meter was proposed based on artificial neural network in this paper. Taking scavenging pressure of air and the engine rotation speed as inputs which were the main factors affecting the effective optical path of the smoke meter, the back-propagation network was constructed. By training the testing data, the network was used for simulating the effective optical path of the smoke meter. The results indicate that the deviations between the simulating and testing data range from - 0. 039 9 to 0. 048 6. In conclusion, the effective optical path of the smoke meter can be calculated by the artificial neural network exactly and practically. |
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| Keywords: | artificial neural network smoke meter effective optical path scavenging pressure algorithm |
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