[1]
D.B. Fogel, T. Fukuda, L. Guan eds.: Special Issue on Computational Intelligence, Proceedings of the IEEE, Vol. 87, No. 9, 1415-1691?, (1999).
Google Scholar
[2]
Shaofei Jiang, Zhaoqi Wu. Structural Health Monitoring and Intelligent Information Processing technology and applications [M], China Construction Industry Press, Beijing, (2011).
Google Scholar
[3]
P.F. Alvanitopoulos, I. Andreadis, A. Elenas: Neuro-fuzzy techniques for the classification of earthquake damages in buildings [J], Measurement 2010, 43: 797–809.
DOI: 10.1016/j.measurement.2010.02.011
Google Scholar
[4]
Seif E. Hamdi, Alain Le Duff, etc.: Acoustic emission pattern recognition approach based on Hilbert–Huang transform for structural health monitoring in polymer-composite materials[J], Applied Acoustics, 2013, 74: 746–757.
DOI: 10.1016/j.apacoust.2012.11.018
Google Scholar
[5]
Zhang Li, Zhang Yu, Structural damage pattern recognition based on fuzzy theory [J], Journal of Northwestern Polytechnic University, 2009, VoL 29, No. 2: 177-183.
Google Scholar
[6]
Qiang Liu, Xun Chen, Nabil Gindy: Fuzzy pattern recognition of AE signals for grinding burn [J], International Journal of Machine Tools & Manufacture, 2005, 45: 811–818.
DOI: 10.1016/j.ijmachtools.2004.11.002
Google Scholar
[7]
M.M. Reda Taha, J. Lucero: Damage identification for structural health monitoring using fuzzy pattern recognition [J], Engineering Structures, 2005, 27: 1774–1783.
DOI: 10.1016/j.engstruct.2005.04.018
Google Scholar
[8]
Samuel da Silva, Milton Dias Ju ´ nior, Vicente Lopes Junior, etc.: Structural damage detection by fuzzy clustering, Mechanical Systems and Signal Processing, 2008, 22: 1636 – 1649.
DOI: 10.1016/j.ymssp.2008.01.004
Google Scholar
[9]
Z. Zhao, C. Chen: Concrete bridge deterioration diagnosis using fuzzy inference system [J], Advances in Engineering Software, 2001, 32: 317-325.
DOI: 10.1016/s0965-9978(00)00089-2
Google Scholar
[10]
Prashant M. Pawar, Ranjan Ganguli: Genetic fuzzy system for damage detection in beams and helicopter rotor blades [J], Computer Methods in Applied Mechanics and Engineering, 2003, 192: 2031–(2057).
DOI: 10.1016/s0045-7825(03)00237-8
Google Scholar
[11]
Basir O, Yuan X. Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory. Information Fusion, 2007; 8: 379–86.
DOI: 10.1016/j.inffus.2005.07.003
Google Scholar
[12]
Diao yangsong, Tong xianneng. Structural damage identification based on information fusion technology 2011: 6841-6844.
Google Scholar
[13]
Jianhua Zhao, Ling Zhang. Structural Damage Localization Using D-S Evidence Theory[C], Applied Mechanics and Materials , Vol. 105—107, 2012: 999-1003.
DOI: 10.4028/www.scientific.net/amm.105-107.999
Google Scholar
[14]
Z. Pawlak: Rough Sets. International Journal of Computer and Information Sciences, 1982: 341-356.
Google Scholar
[15]
Pan Wei, Xia Huabing, San Ye. Rough Set Theory based on Genetic Algorithm in Radar Equipment Damage. Chinese Control and Decision Conference, (2008).
DOI: 10.1109/ccdc.2008.4597535
Google Scholar
[16]
Ning Li, Rui Zhou, Qinghua Hu, Xiaohang Liu. Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine. Mechanical Systems and Signal Processing , 2012, 28: 608–621.
DOI: 10.1016/j.ymssp.2011.10.016
Google Scholar
[17]
Yeong Min Kim, Chee Kyeong Kim, Jae Cheol Lee. Rough set algorithm for crack category determination of reinforced concrete structures. Advances in Engineering Software , 2009, 40: 202–211.
DOI: 10.1016/j.advengsoft.2008.04.002
Google Scholar
[18]
Santosh Kumar Mandal, Felix T.S. Chan, M.K. Tiwari. Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM. Expert Systems with Applications, 2012 39: 3071–3080.
DOI: 10.1016/j.eswa.2011.08.170
Google Scholar
[19]
Xiaoran Zhu, Youyun Zhang, Yongsheng Zhu. Bearing performance degradation assessment based on the rough support vector data description, Mechanical Systems and Signal Processing, 2013, 34: 203–217.
DOI: 10.1016/j.ymssp.2012.08.008
Google Scholar
[20]
Zeidenberg, Matthew. Neural Networks in Artificial Intelligence. 1990: Ellis Horwood Limited. 1990. ISBN 0-13-612185-3.
Google Scholar
[21]
Widodo Achmad, Yang Bo-Suk. Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors[J]. Expert Systems with Applications, 2007, 33(1): 241-250.
DOI: 10.1016/j.eswa.2006.04.020
Google Scholar
[22]
Venkatasubramanian V and Chan K. A neural network methodology for process fault diagnosis[J]. Journal of AIChE, 1989, 35(12): 1993-(2002).
DOI: 10.1002/aic.690351210
Google Scholar
[23]
C.Y. Kao, Shih-Lin Hung. Detection of structural damage via free vibration responses generated by approximating artificial neural networks. Computers & Structures, 2003, 81(28-19): 2631-2644.
DOI: 10.1016/s0045-7949(03)00323-7
Google Scholar
[24]
Deyin Ma, Yanchun Liang, Xiaoshe Zhao, Renchu Guan, Xiaohu Shi. Multi-BP expert system for fault diagnosis of power system, Engineering Applications of Artificial Intelligence, 2013, 26(3): 937-944.
DOI: 10.1016/j.engappai.2012.03.017
Google Scholar
[25]
Jiang Shaofei, Structure optimization and damage detection based on neural network [M] Science press, Beijing, (2002).
Google Scholar
[26]
Li Hui, Bao Yuequan, Ou Jinping. Structural damage identification based on integration of information fusion and Shannon entropy [J], Mechanical Systems and Signal Processing, 2008, 22(6): 1427-1440.
DOI: 10.1016/j.ymssp.2007.11.025
Google Scholar
[27]
Yang Jie, Li Aiqun, Miao Changqing, Application of BP Neural network to cable damage idenification for long span cable-stayed bridges [J], China Civil Engineering Journal, 2006, 39(5): 72-77.
Google Scholar
[28]
Zenon Waszczyszyn, Leonard Ziemianski. Neural networks in mechanics of structures and materials : new results and prospects of applications. Computer and Structures, 2001, 79: 2261-2276.
DOI: 10.1016/s0045-7949(01)00083-9
Google Scholar
[29]
Mingqiao Xu, Lili Wang. A new method for studying the dynamic response and damage evolution of polymers at high strain rates. Mechanics of Materials, 2006, 38: 68–75.
DOI: 10.1016/j.mechmat.2005.05.010
Google Scholar
[30]
R.A. Saeed, A.N. Galybin, V. Popov. 3D fluid–structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS. Mechanical Systems and Signal Processing, 2013, 34: 259–276.
DOI: 10.1016/j.ymssp.2012.08.004
Google Scholar
[31]
Ali Nazari, Amir Ali Milani, Gholamreza Khalaj. Modeling ductile to brittle transition temperature of functionally graded steels by ANFIS. Applied Mathematical Modelling, 2012, 36: 3903–3915.
DOI: 10.1016/j.apm.2011.11.032
Google Scholar
[32]
Karim Salahshoor, Mojtaba Kordestani, Majid S. Khoshro. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers. Energy, 2010, 35(12): 5472-5482.
DOI: 10.1016/j.energy.2010.06.001
Google Scholar
[33]
Donald F. Specht, Probabilistic neural networks, Neural Networks, 1990, 3(1): 109–118.
Google Scholar
[34]
Jiang Shaofei, Yang Xiaonian et. al, Research on damage localization of principle component analysis based probabilistic neural network [J], earthquake engineering and engineering vibration, 2004, 24(2): 187-191.
Google Scholar
[35]
Shao-Fei Jiang. Damage identification of a steel structure based on wavelet probabilistic neural network, 2005,37:561-564.
Google Scholar
[36]
Yan, Yun-Ju Yang, Hai-Feng Qin, Xiao-Wen. Damage patterns recognition study based on adaptive probabilistic neural network. Journal of Vibration and Shock, 2008, 27(7): 8-12.
Google Scholar
[37]
C. Cortes, V. Vapnik. Support Vector Networks.Machine Learning, 1995, 20: 273-297.
Google Scholar
[38]
Huang Hongmei, Yuan Shenfang, Chang Qi, Research on impact damage identification based on fiber Bragg grating and the support vector machine, Journal of vibration and shock, 2010, 29(10): 53-55.
Google Scholar
[39]
Hassen Keskes, Ahmed Braham, Zied Lachiri. Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM, Electric Power Systems Research, 2013, 97: 151– 157.
DOI: 10.1016/j.epsr.2012.12.013
Google Scholar
[40]
Sun Deshan, Research on the classification and regression algorithm of support vector machine [D], (2004).
Google Scholar
[41]
Fu Chunyu, Shan Deshan, Li Qiao, Static damage identification method based on support vector machine, China Railway Science, 2010, 31(5): 47-53.
Google Scholar
[42]
Diego F., David M., Oscar F., Amparo A. . Automatic bearing fault diagnosis based on one-class ν-SVM, Computers & Industrial Engineering, 2013, 64: 357–365.
DOI: 10.1016/j.cie.2012.10.013
Google Scholar
[43]
Cai, Chenning. A GA approach for damage localization using scattered lamb wave, Applied Mechanics and Materials, 2013, 275: 2579-2584.
DOI: 10.4028/www.scientific.net/amm.275-277.2579
Google Scholar
[44]
H.Y. Guo, Z.L. Li. A two-stage method to identify structural damage sites and extents by using evidence theory and micro-search genetic algorithm. Mechanical Systems and Signal Processing, 2009, 23(3): 769-782.
DOI: 10.1016/j.ymssp.2008.07.008
Google Scholar
[45]
Fei Kang, Jun-jie Li, Qing Xu. Damage detection based on improved particle swarm optimization using vibration data. Applied Soft Computing, 2012, 12: 2329–2335.
DOI: 10.1016/j.asoc.2012.03.050
Google Scholar
[46]
S.M. Seyedpoor. A two stage method for structural damage detection using a modal strain energy based index and particle swarm optimization. International Journal of Non-Linear Mechanics, 2012, 47: 1–8.
DOI: 10.1016/j.ijnonlinmec.2011.07.011
Google Scholar
[47]
Amir Hossein Gandomi, Gun Jin Yun, etc. Chaos-enhanced accelerated particle swarm optimization. Commun Nonlinear Sci Numer Simulat, 2013, 18: 327–340.
DOI: 10.1016/j.cnsns.2012.07.017
Google Scholar