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Structural Engineering and Mechanics Volume 45, Number 1, January10 2013, pages 05367 DOI: http://dx.doi.org/10.12989/sem.2013.45.1.053 


Modeling and assessment of VWNN for signal processing of structural systems 

JengWen Lin and TzungHan Wu


Abstract  
This study aimed to develop a model to accurately predict the acceleration of structural systems during an earthquake. The acceleration and applied force of a structure were measured at current time step and the velocity and displacement were estimated through linear integration. These data were used as input to predict the structural acceleration at next time step. The computation tool used was the Volterra/Wiener neural network (VWNN) which contained the mathematical model to predict the acceleration. For alleviating problems of relatively largedimensional and nonlinear systems, the VWNN model was utilized as the signal processing tool, including the Taylor series components in the input nodes of the neural
network. The number of the intermediate layer nodes in the neural network model, containing the training and simulation stage, was evaluated and optimized. Discussions on the influences of the gradient descent with adaptive learning rate algorithm and the LevenbergMarquardt algorithm, both for determining the network weights, on prediction errors were provided. During the simulation stage, different earthquake excitations were tested with the optimized settings acquired from the training stage to find out which of the algorithms would result in the smallest error, to determine a proper simulation model.  
Key Words  
gradient descent with adaptive learning rate algorithm; Levenberg/Marquardt algorithm; modeling; Taylor series; Volterra/Wiener neural network  
Address  
JengWen Lin and TzungHan Wu: Department of Civil Engineering, Feng Chia University, Taichung 407, Taiwan, R.O.C.  