최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Measurement 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)
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...
[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] 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] 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] 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] 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
[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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.