Title :
Prediction of muffler flow regeneration noise with neural network
Author :
Zhao, Haijun ; Deng, Zhaoxiang ; Zhao, Shiju ; Yang, Jie
Author_Institution :
State Key Lab. of Mech. Transm., Chongqing Univ., Chongqing, China
Abstract :
Flow regeneration noise is a main reason effect on attenuation performance of mufflers, at present no sophisticated software or tool is found to predict effectively flow regeneration noises from mufflers. Prediction of flow regeneration noise from a muffler element of simple expansion chamber is realized using Bp neural network, and comparison of prediction with experiment is carried out. Results show that they agree well, forecasting precision is high, and that fuzzy normal establishment program work is avoided. Significant foundation is provided for researching on production mechanism of flow regeneration noise from mufflers and improving attenuation performance.
Keywords :
acoustic noise; backpropagation; exhaust systems; mechanical engineering computing; neural nets; silencers; BP neural network; attenuation performance; fuzzy normal establishment program; muffler flow regeneration noise prediction; Artificial intelligence; Artificial neural networks; Attenuation measurement; Exhaust systems; Fluid flow measurement; Mathematical model; Neural networks; Predictive models; Production; Software tools; flow regeneration noise; muffler; neural network; prediction;
Conference_Titel :
Electronic Measurement & Instruments, 2009. ICEMI '09. 9th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-3863-1
Electronic_ISBN :
978-1-4244-3864-8
DOI :
10.1109/ICEMI.2009.5274293