Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) for structural damage identification
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[1] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[2] Ranjan Ganguli,et al. Composite material and piezoelectric coefficient uncertainty effects on structural health monitoring using feedback control gains as damage indicators , 2011 .
[3] B. Karaağaç,et al. Predicting optimum cure time of rubber compounds by means of ANFIS , 2012 .
[4] Mun-Bo Shim,et al. Crack identification using neuro-fuzzy-evolutionary technique , 2002 .
[5] S. Talukdar,et al. A comparative study of compressive, flexural, tensile and shear strength of concrete with fibres of different origins , 2007 .
[6] Ranjan Ganguli,et al. Structural Damage Detection Using Modal Curvature and Fuzzy Logic , 2009 .
[7] Chung Bang Yun,et al. Neural networks-based damage detection for bridges considering errors in baseline finite element models , 2003 .
[8] Jin H. Huang,et al. Detection of cracks using neural networks and computational mechanics , 2002 .
[9] Hossein Nezamabadi-pour,et al. Application of the ANFIS model in deflection prediction of concrete deep beam , 2013 .
[10] Marta B. Rosales,et al. Crack detection in beam-like structures , 2009 .
[11] Yi-Qing Ni,et al. Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks , 2006 .
[12] Enrico Zio,et al. A neuro-fuzzy technique for fault diagnosis and its application to rotating machinery , 2009, Reliab. Eng. Syst. Saf..
[13] Jyh-Shing Roger Jang,et al. Self-learning fuzzy controllers based on temporal backpropagation , 1992, IEEE Trans. Neural Networks.
[14] Ri Levin,et al. DYNAMIC FINITE ELEMENT MODEL UPDATING USING NEURAL NETWORKS , 1998 .
[15] E. Salajegheh,et al. Optimal Design of Geometrically Nonlinear Space Trusses Using an Adaptive Neuro-Fuzzy Inference System , 2009 .
[16] Martin T. Hagan,et al. Neural network design , 1995 .
[17] Mohd Saleh Jaafar,et al. An approach to predict ultimate bearing capacity of surface footings using artificial neural network , 2008 .
[18] Fi-John Chang,et al. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .
[19] Ardeshir Bahreininejad,et al. Damage detection of truss bridge joints using Artificial Neural Networks , 2008, Expert Syst. Appl..
[20] H. Joel Trussell,et al. Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis , 1999, IEEE Trans. Ind. Electron..
[21] H. Abdul Razak,et al. Damage detection of steel bridge girder using Artificial Neural Networks , 2012 .
[22] Seung-Chang Lee,et al. Prediction of concrete strength using artificial neural networks , 2003 .
[23] Ying-Ming Wang,et al. An adaptive neuro-fuzzy inference system for bridge risk assessment , 2008, Expert Syst. Appl..
[24] Peter Avitabile,et al. Efficient techniques for forced response involving linear modal components interconnected by discrete nonlinear connection elements , 2009 .
[25] Chih-Chen Chang,et al. Selection of training samples for model updating using neural networks , 2002 .
[26] M. M. Reda Taha,et al. Damage identification for structural health monitoring using fuzzy pattern recognition , 2005 .
[27] Ranjan Ganguli,et al. Health monitoring of a helicopter rotor in forward flight using fuzzy logic , 2002 .
[28] S.J.S. Hakim,et al. Development and Applications of Artificial Neural Network for Prediction of Ultimate Bearing Capacity of Soil and Compressive Strength of Concrete , 2006 .
[29] S. Sivanandam,et al. Introduction to Fuzzy Logic using MATLAB , 2006 .
[30] Hossein Nezamabadi-pour,et al. Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system , 2011 .
[31] Michio Sugeno,et al. Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.
[32] Abdulkadir Çevik,et al. Neuro-fuzzy modeling of rotation capacity of wide flange beams , 2011, Expert Syst. Appl..
[33] Myung-Won Suh,et al. Crack Identification Using Hybrid Neuro-Genetic Technique , 2000 .
[34] Dayal R. Parhi,et al. Smart crack detection of a cracked cantilever beam using fuzzy logic technology with hybrid membership functions , 2011 .
[35] Jyh-Shing Roger Jang,et al. ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..
[36] Qiao Hu,et al. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs , 2007 .
[37] Ömer Civalek. Flexural and axial vibration analysis of beams with different support conditions using artificial neural networks , 2004 .
[38] Shi-jie Zheng,et al. A genetic fuzzy radial basis function neural network for structural health monitoring of composite laminated beams , 2011, Expert Syst. Appl..
[39] Marley M. B. R. Vellasco,et al. A neuro-fuzzy evaluation of steel beams patch load behaviour , 2008, Adv. Eng. Softw..
[40] C. S. Cai,et al. Application of artificial neural networks to the response prediction of geometrically nonlinear truss structures , 2007 .
[41] Michio Sugeno,et al. Industrial Applications of Fuzzy Control , 1985 .
[42] Xiao Zhi Gao,et al. Soft computing methods in motor fault diagnosis , 2001, Appl. Soft Comput..
[43] Lotfi A. Zadeh,et al. Fuzzy Sets , 1996, Inf. Control..
[44] A. N. Galybin,et al. Crack identification in curvilinear beams by using ANN and ANFIS based on natural frequencies and frequency response functions , 2011, Neural Computing and Applications.
[45] Mohd Saleh Jaafar,et al. Application of Artificial Neural Networks to Predict Compressive Strength of High Strength Concrete , 2011 .
[46] Yong Lu,et al. A two-level neural network approach for dynamic FE model updating including damping , 2004 .
[47] Ayhan am,et al. A model of adaptive neural-based fuzzy inference system (ANFIS) for prediction of friction coefficient in open channel flow , 2011 .
[48] S. Kumar,et al. Neuro-fuzzy approaches for pipeline condition assessment , 2007 .
[49] D. Jeng,et al. Estimation of pile group scour using adaptive neuro-fuzzy approach , 2007 .
[50] Hosein Naderpour,et al. Prediction of FRP-confined compressive strength of concrete using artificial neural networks , 2010 .