Acoustic performance of exhaust muffler based genetic algorithms and artificial neural network

被引：0

作者：

Wang, Bing ^{[1
]}

Wang, Xiaoli ^{[1
]}

机构：

[1] School of mechanical engineering, Huaihai institute of technology

来源：

Telkomnika | 2013年 / 11卷 / 02期

关键词：

Acoustic performance; Genetic algorithm; Neural network; Noise;

D O I：

10.12928/telkomnika.v11i2.931

中图分类号：

学科分类号：

摘要：

The noise level was one of the important indicators as a measure of the quality and performance of the diesel engine. Exhaust noise in diesel engines machine accounted for an important proportion of installed performance exhaust muffler and it was an effective way to control exhaust noise. This article using orthogonal test program for the muffler structure parameters as input to the sound pressure level and diesel fuel each output artificial neural network (BP network) learning sample. Matlab artificial neural network toolbox to complete the training of the network, and better noise performance and fuel consumption rate performance muffler internal structure parameters combination was obtained through genetic algorithm gifted collaborative validation of artificial neural networks and genetic algorithms to optimize application exhaust muffler design is entirely feasible.

引用

页码：313 / 320

页数：7

共 16 条

[1]

Sullivan J.W., Crocker M.J., Analysis of concentric tube resonators having unpartitioned cavities, Acoust Soc, 64, pp. 207-222, (1978)

[2]

Sullivan J.W., A method of modeling perforated tube muffler components I: theory, Acoust Soc, 66, pp. 772-780, (1979)

[3]

Jayaraman K., Yam K., Decoupling approach to modeling perforated tube muffler component, Acoust Soc, 69, 2, pp. 390-396, (1981)

[4]

Thawani P.T., Jayaraman K., Modeling and applications of straight-through resonators, Acoust Soc, 73, 4, pp. 1387-1389, (1983)

[5]

Munjal M.L., Rao K.N., Sahasrabudhe A.D., Aeroacoustic analysis of perforated muffler components, J Sound Vib, 114, 2, pp. 173-190, (1987)

[6]

Shuzhan B., Guoxiang L., Wang H., Based on neural network and the optimization design method of exhaust muffler, Journal of agricultural machinery, 36, 12, pp. 153-155, (2005)

[7]

Feng C., Diseal exhaust design based artificial neural network Thesis, (2002)

[8]

Guosheng Z., Research and software development of optima design of automobile exhaust muffler based on genetic algorithm, (2005)

[9]

Xinsheng L., Combination of GA and BP Networks for Optimization, Journal of Guizhou University (Natural Science), 21, 2, pp. 179-184, (2004)

[10]

Yuehua T., Youming X., Optimization Research of BP Neural Network and Genetic Algorithm Based on Numerical Calculation Method. Thesis., (2006)

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