$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

Sound quality prediction of vehicle interior noise and mathematical modeling using a back propagation neural network (BPNN) based on particle swarm optimization (PSO)

Measurement science & technology, v.27 no.1, 2016년, pp.015801 -   

Zhang, Enlai (Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, People’s Republic of China) ,  Hou, Liang (Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, People’s Republic of China) ,  Shen, Chao (Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, People’s Republic of China) ,  Shi, Yingliang (Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, People’s Republic of China) ,  Zhang, Yaxiang (Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, People’s Republic of China)

Abstract AI-Helper 아이콘AI-Helper

To better solve the complex non-linear problem between the subjective sound quality evaluation results and objective psychoacoustics parameters, a method for the prediction of the sound quality is put forward by using a back propagation neural network (BPNN) based on particle swarm optimization (PSO...

참고문헌 (36)

  1. [1] Wang Y S, Shen G Q and Xing Y F 2014 A sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network Mech. Syst. Signal Process. 45 255 10.1016/j.ymssp.2013.11.001 A sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network Wang Y S, Shen G Q and Xing Y F Mech. Syst. Signal Process. 45 2014 255 

  2. [2] Wang Y S, Lee C M, Kim D G and Xu Y 2006 Sound-quality prediction for nonstationary vehicle interior noise based on wavelet pre-processing neural network model J. Sound Vib. 299 933 10.1016/j.jsv.2006.07.034 Sound-quality prediction for nonstationary vehicle interior noise based on wavelet pre-processing neural network model Wang Y S, Lee C M, Kim D G and Xu Y J. Sound Vib. 299 2006 933 

  3. [3] Payri F, Broatch A, Margot X and Monelletta L 2009 Sound quality assessment of diesel combustion noise using in-cylinder pressure components Meas. Sci. Technol. 20 015107 10.1088/0957-0233/20/1/015107 Sound quality assessment of diesel combustion noise using in-cylinder pressure components Payri F, Broatch A, Margot X and Monelletta L Meas. Sci. Technol. 0957-0233 20 1 015107 2009 

  4. [4] Jeong U C, Kim J S, Jeong J E, Yang I H and Oh J E 2015 Development of a sound quality index for the wash cycle process of front-loading washing machines considering the impacts of individual noise sources Appl. Acoust. 87 183 10.1016/j.apacoust.2014.06.001 Development of a sound quality index for the wash cycle process of front-loading washing machines considering the impacts of individual noise sources Jeong U C, Kim J S, Jeong J E, Yang I H and Oh J E Appl. Acoust. 0003-682X 87 2015 183 

  5. [5] Gao Y H, Sun Q, Liang J and Tang R J 2010 Evaluation method and mathematical model of vehicle interior sound quality during acceleration J. Jinlin Univ. 40 1502 10.13229/j.cnki.jdxbgxb2010.06.028 Evaluation method and mathematical model of vehicle interior sound quality during acceleration Gao Y H, Sun Q, Liang J and Tang R J J. Jinlin Univ. 40 2010 1502 

  6. [6] Jin C, Zhou H and Hou Y F 2012 Vehicle interior annoyance evaluation based on psychoacoustic indexes J. Vib. Shock 31 86 10.13465/j.cnki.jvs.2012.11.015 Vehicle interior annoyance evaluation based on psychoacoustic indexes Jin C, Zhou H and Hou Y F J. Vib. Shock 31 2012 86 

  7. [7] Liu H, Zhang J H, Guo P, Bi F R, Yu H Z and Ni G J 2015 Sound quality prediction for engine-radiated noise Mech. Syst. Signal Process. 56–57 277 10.1016/j.ymssp.2014.10.005 Sound quality prediction for engine-radiated noise Liu H, Zhang J H, Guo P, Bi F R, Yu H Z and Ni G J Mech. Syst. Signal Process. 56–57 2015 277 

  8. [8] Shen X M, Zuo S G, He L R, Chen R F and Zhang J F 2011 Weight of objective evaluation parameters for fuel cell vehicle sound quality J. Vib. Shock. 30 91 10.13465/j.cnki.jvs.2011.01.033 Weight of objective evaluation parameters for fuel cell vehicle sound quality Shen X M, Zuo S G, He L R, Chen R F and Zhang J F J. Vib. Shock. 30 2011 91 

  9. [9] Zhao T H, Lu B W, Jiang W J, Sun Q and Liang J 2012 Evaluation for car interior noise quality preference J. Xi’an Jiaotong Univ. 46 127 Evaluation for car interior noise quality preference Zhao T H, Lu B W, Jiang W J, Sun Q and Liang J J. Xi’an Jiaotong Univ. 46 2012 127 

  10. [10] Marashdeh Q, Warsito W, Fan L S and Teixeira F L 2006 A nonlinear image reconstruction technique for ECT using a combined neural network approach Meas. Sci. Technol. 17 2097 10.1088/0957-0233/17/8/007 A nonlinear image reconstruction technique for ECT using a combined neural network approach Marashdeh Q, Warsito W, Fan L S and Teixeira F L Meas. Sci. Technol. 0957-0233 17 8 007 2006 2097 

  11. [11] Hong H S, Shim S R and Han H S 2011 Developing the logic for evaluating the indoor noise of a naval vessel using a back-propagation neural network J. Mech. Sci. Technol. 25 2755 10.1007/s12206-011-0804-2 Developing the logic for evaluating the indoor noise of a naval vessel using a back-propagation neural network Hong H S, Shim S R and Han H S J. Mech. Sci. Technol. 25 2011 2755 

  12. [12] Brindisi A and Concilio A 2008 Passengers’ comfort modeling inside aircraft J. Aircraft. 45 2001 10.2514/1.36305 Passengers’ comfort modeling inside aircraft Brindisi A and Concilio A J. Aircraft. 0021-8669 45 2008 2001 

  13. [13] Li F and Zuo Y Y 2013 Sound quality evaluation control of car interior noise Autom. Control Mechatronic Eng. II 415 569 10.4028/www.scientific.net/AMM.415.569 Sound quality evaluation control of car interior noise Li F and Zuo Y Y Autom. Control Mechatronic Eng. II 415 2013 569 

  14. [14] Gao Y H, Tang R J, Liang J, Zhao T H and Zhang L T 2013 Sound quality prediction and weight analysis of vehicles on GA-BP neural network Opt. Precis. Eng. 21 462 10.3788/OPE.20132102.0462 Sound quality prediction and weight analysis of vehicles on GA-BP neural network Gao Y H, Tang R J, Liang J, Zhao T H and Zhang L T Opt. Precis. Eng. 21 2013 462 

  15. [15] Chen X, Hu H L, Liu F and Gao X X 2011 Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm Meas. Sci. Technol. 22 104008 10.1088/0957-0233/22/10/104008 Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm Chen X, Hu H L, Liu F and Gao X X Meas. Sci. Technol. 0957-0233 22 10 104008 2011 

  16. [16] Xiong T, Bao Y K, Hu Z Y and Chiong R 2015 Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms Inf. Sci. 305 77 10.1016/j.ins.2015.01.029 Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms Xiong T, Bao Y K, Hu Z Y and Chiong R Inf. Sci. 305 2015 77 

  17. [17] Nieto P J G, Garcia-Gonzalo E, Lasheras F S and Juez F J G 2015 Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability Reliab. Eng. Syst. Saf. 138 219 10.1016/j.ress.2015.02.001 Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability Nieto P J G, Garcia-Gonzalo E, Lasheras F S and Juez F J G Reliab. Eng. Syst. Saf. 0951-8320 138 2015 219 

  18. [18] Das G, Pattnaik P K and Padhy S K 2014 Artificial neural network trained by particle swarm optimization for nonlinear channel equalization Expert Syst. Appl. 41 3491 10.1016/j.eswa.2013.10.053 Artificial neural network trained by particle swarm optimization for nonlinear channel equalization Das G, Pattnaik P K and Padhy S K Expert Syst. Appl. 0957-4174 41 2014 3491 

  19. [19] Meng Z G, Xu Y, Zheng Y C, Zhu Y C, Jia Y and Chen S B 2014 Inversion of lunar regolith layer thickness with CELMS data using BPNN method Planet. Space Sci. 101 1 10.1016/j.pss.2014.05.020 Inversion of lunar regolith layer thickness with CELMS data using BPNN method Meng Z G, Xu Y, Zheng Y C, Zhu Y C, Jia Y and Chen S B Planet. Space Sci. 0032-0633 101 2014 1 

  20. [20] Yang C, Yu D J and Xu Y J 2013 Sound quality prediction for vehicle door-slamming noise based on empirical mode decomposition and back propagation neural network Autom. Eng. 35 457 10.1016/j.automatica.2012.11.004 Sound quality prediction for vehicle door-slamming noise based on empirical mode decomposition and back propagation neural network Yang C, Yu D J and Xu Y J Autom. Eng. 35 2013 457 

  21. [21] Concilio A and Sorrentino A 1996 Use of artificial neural networks as estimators and controllers 3rd Int. Conf. on Intelligent Materials/3rd European Conf. on Smart Structures and Materials vol 2779 p 897 10.1117/12.237075 Use of artificial neural networks as estimators and controllers Concilio A and Sorrentino A 3rd Int. Conf. on Intelligent Materials/3rd European Conf. on Smart Structures and Materials 2779 1996 897 

  22. [22] Prasad R, Pandey A, Singh K P, Sing V P, Mishra R K and Singh D 2012 Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: a comparison of different transfer functions Adv. Space Res. 50 363 10.1016/j.asr.2012.04.010 Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: a comparison of different transfer functions Prasad R, Pandey A, Singh K P, Sing V P, Mishra R K and Singh D Adv. Space Res. 0273-1177 50 2012 363 

  23. [23] Leung F H F, Lam H K, Ling S H and Tam P K S 2003 Tuning of the structure and parameters of a neural network using an improved genetic algorithm IEEE Trans. Neural Netw. 14 79 10.1109/TNN.2002.804317 Tuning of the structure and parameters of a neural network using an improved genetic algorithm Leung F H F, Lam H K, Ling S H and Tam P K S IEEE Trans. Neural Netw. 1045-9227 14 2003 79 

  24. [24] Cui C C, Li B, Huang F G and Zhang R C 2007 Genetic algorithm-based form error evaluation Meas. Sci. Technol. 18 1818 10.1088/0957-0233/18/7/004 Genetic algorithm-based form error evaluation Cui C C, Li B, Huang F G and Zhang R C Meas. Sci. Technol. 0957-0233 18 7 004 2007 1818 

  25. [25] Biesinger B, Hu B and Raidl G 2015 A hybrid genetic algorithm with solution archive for the discrete-centroid problem J. Heuristics 21 391 10.1007/s10732-015-9282-5 A hybrid genetic algorithm with solution archive for the discrete-centroid problem Biesinger B, Hu B and Raidl G J. Heuristics 1381-1231 21 2015 391 

  26. [26] Yang Y Q, Wang G J and Yang Y 2015 Parameters optimization of polygonal fuzzy neural networks based on GA-BP hybrid algorithm Int. J. Mach. Learn. Cybern. 5 815 10.1007/s13042-013-0224-y Parameters optimization of polygonal fuzzy neural networks based on GA-BP hybrid algorithm Yang Y Q, Wang G J and Yang Y Int. J. Mach. Learn. Cybern. 5 2015 815 

  27. [27] Park H S and Anh T V 2013 Development of two-phase neural network-genetic algorithm hybrid model in modeling damage evolution in roll forming of aluminum sheet Int. J. Mater. Forming 6 423 10.1007/s12289-012-1096-5 Development of two-phase neural network-genetic algorithm hybrid model in modeling damage evolution in roll forming of aluminum sheet Park H S and Anh T V Int. J. Mater. Forming 6 2013 423 

  28. [28] Natarajan U, Periasamy V M and Saravanan R 2007 Application of particle swarm optimization in artificial neural network for the prediction of tool life Int. J. Adv. Manuf. Technol. 31 871 10.1007/s00170-005-0252-1 Application of particle swarm optimization in artificial neural network for the prediction of tool life Natarajan U, Periasamy V M and Saravanan R Int. J. Adv. Manuf. Technol. 0268-3768 31 2007 871 

  29. [29] Ma X Y, Duan Y F, Liu M and Li H F 2012 Prediction of pressure drop of coke water slurry flowing in pipeline by PSO-BP neural network Proc. CSEE 32 54 10.13334/j.0258-8013.pcsee.2012.05.005 Prediction of pressure drop of coke water slurry flowing in pipeline by PSO-BP neural network Ma X Y, Duan Y F, Liu M and Li H F Proc. CSEE 0258-8013 32 2012 54 

  30. [30] Jin C, Jin S W and Qin L N 2012 Attribute selection method based on a hybrid BPNN and PSO algorithms Appl. Soft Comput. 12 2147 10.1016/j.asoc.2012.03.015 Attribute selection method based on a hybrid BPNN and PSO algorithms Jin C, Jin S W and Qin L N Appl. Soft Comput. 12 2012 2147 

  31. [31] Bai T C, Meng H B and Yao J H 2014 A forecasting method of forest pests based on the rough set and PSO-BP neural network Neural Comput. Appl. 25 1699 10.1007/s00521-014-1658-1 A forecasting method of forest pests based on the rough set and PSO-BP neural network Bai T C, Meng H B and Yao J H Neural Comput. Appl. 25 2014 1699 

  32. [32] Parizet E 2002 Paired comparison listening tests and circular error rates Acta Acust. United Acust. 88 594 Paired comparison listening tests and circular error rates Parizet E Acta Acust. United Acust. 88 2002 594 

  33. [33] Parsa B G, Maafi A A, Haghdoost A, Arabi Y, Khojamli M, Chatrnour G and Bidari A 2014 The validity and reliability of the persian version of the revised fibromyalgia impact questionnaire Rheumatol. Int. 34 175 10.1007/s00296-013-2929-3 The validity and reliability of the persian version of the revised fibromyalgia impact questionnaire Parsa B G, Maafi A A, Haghdoost A, Arabi Y, Khojamli M, Chatrnour G and Bidari A Rheumatol. Int. 34 2014 175 

  34. [34] Binu D 2015 Cluster analysis using optimization algorithms with newly designed objective functions Expert Syst. Appl. 42 5848 10.1016/j.eswa.2015.03.031 Cluster analysis using optimization algorithms with newly designed objective functions Binu D Expert Syst. Appl. 0957-4174 42 2015 5848 

  35. [35] Kim E Y, Shin T J and Lee S K 2013 Sound quality index for assessment of sound quality of laser printers based on a combination of sound metrics J. Noise Control Eng. 61 534 10.3397/1/3761047 Sound quality index for assessment of sound quality of laser printers based on a combination of sound metrics Kim E Y, Shin T J and Lee S K J. Noise Control Eng. 61 2013 534 

  36. [36] Ghose D K, Panda S S and Swain P C 2010 Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks J. Hydrol. 394 296 10.1016/j.jhydrol.2010.09.003 Prediction of water table depth in western region, Orissa using BPNN and RBFN neural networks Ghose D K, Panda S S and Swain P C J. Hydrol. 0022-1694 394 2010 296 

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로