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Smart Structures and Systems Volume 15, Number 4, April 2015 , pages 1139-1158 DOI: https://doi.org/10.12989/sss.2015.15.4.1139 |
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PCA-based neuro-fuzzy model for system identification of smart structures |
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Soroush Mohammadzadeh, Yeesock Kim and Jaehun Ahn
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| Abstract | ||
| This paper proposes an efficient system identification method for modeling nonlinear behavior of civil structures. This method is developed by integrating three different methodologies: principal component analysis (PCA), artificial neural networks, and fuzzy logic theory, hence named PANFIS (PCA-based adaptive neuro-fuzzy inference system). To evaluate this model, a 3-story building equipped with a magnetorheological (MR) damper subjected to a variety of earthquakes is investigated. To train the input-output function of the PANFIS model, an artificial earthquake is generated that contains a variety of characteristics of recorded earthquakes. The trained model is also validated using the1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes. The adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. It is demonstrated from the training and validation processes that the proposed PANFIS model is effective in modeling complex behavior of the smart building. It is also shown that the proposed PANFIS produces similar performance with the benchmark ANFIS model with significant reduction of computational loads. | ||
| Key Words | ||
| system identification; principal component analysis (PCA); fuzzy logic; neural network; adaptive neuro-fuzzy inference system (ANFIS); earthquake; magnetorheological damper; smart structures | ||
| Address | ||
| Soroush Mohammadzadeh and Yeesock Kim: Department of Civil and Environmental Engineering, Worcester Polytechnic Institute, Worcester, 100 Institute Road, MA01609-2280, USA Jaehun Ahn: School of Civil and Environmental Engineering, Pusan National University, Busan 609-735, South Korea | ||