Neuro Fuzzy Model for Predicting the Dynamic Characteristics of Beams
An adaptive neuro-fuzzy inference system (ANFIS) is introduced to predict the dynamic behavior of beams. The effects of axial forces and large displacements are considered in the analysis. A database of tests for the dynamic characteristics of beams is developed from the experimental tests. The responses of nonlinear vibration force for the single and multiple-stepped beams are calculated from the finite element method (FEM), experimental tests and neuro-fuzzy model for comparison. The neuro-fuzzy model provides a general framework for the combination of neural networks and fuzzy logic. It is more flexible with more options of incorporating the fuzzy nature of the real-world system and is an useful estimation tool for the dynamic characteristics of beams. Therefore, ANFIS can be a useful tool for dynamic behaviour analysis of multiple-stepped beams subjected to axial loads and large displacement.
Key Wordsdynamic analysis large displacement neuro-fuzzy finite element beam
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- 3.Elnashai, B., Izzuddin, P. and Dowling, J., Efficient large displacement elastoplastic dynamic analysis of steel frames. European Earthquake Engineering, 1989, 3: 32–41.Google Scholar
- 4.Zhao, X., Nianli, L. and Bing, L., Dynamic analysis of flexible large displacement beam system with second-order effect. IEEE Computer Society, 2010, 1: 577–580.Google Scholar
- 5.Adam, C. and Krawinkler, H., Large displacement effects on seismically excited elastic-plastic frame structures. Asian Journal of Civil Engineering (Building and Housing), 2004, 5: 41–55.Google Scholar
- 15.Hashamdar, H., Ibrahim, Z. and Jameel, M., Finite element analysis of nonlinear structures withNewmark method. International Journal of the Physical Sciences, 2011, 6(6): 1395–1403.Google Scholar
- 20.Demirdag, O. and Murat, Y., Free vibration analysis of elastically supported timoshenko columns with attached masses using fuzzy neural network. Journal of Scientific & Industrical Research, 2009, 68: 285–291.Google Scholar
- 21.Nassair, Z., Madkour, A., Awadalla, M. and Abdulhady, M., System identification using intelligent algorithms. ASAT, 2009, 13: 1–13.Google Scholar
- 23.Farbod, K., Farhoud, K., Sadeghi, A., Esat, I., Ventura, C. and Silva, C., Structural vibration modeling using a neuro-fuzzy approach. IEEE International Conference on Fuzzy systems, 2006: 685–691.Google Scholar
- 28.Lin, C. and Lee, G., Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Upper Saddle River (NJ): Prentice Hall P T R, 1996.Google Scholar
- 30.Song, Q. and Kasabov, N., Dynamic evolving neural-fuzzy inference system(denfis): online learning and application for time-series prediction. IEEE Transactions on Fuzzy Systems, 2000, 20(10): 144–154.Google Scholar
- 32.Kisi, O., Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol. Sci. J., 2005, 50(4): 683–696.Google Scholar
- 34.Alavandar, S. and Nigam, M.J., Adaptive neuro-fuzzy inference system based control of six DOF robot manipulator. Journal of Engineering Science and Technology Review, 2008, 1: 106–111.Google Scholar
- 35.Schwarz, B. and Richardson, M., Experimental Model Analysis. CSI Reliability Week, Orlando, FL., Press, 1996: 1–12.Google Scholar