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Structural Engineering and Mechanics
  Volume 45, Number 1, January10 2013, pages 053-67

Modeling and assessment of VWNN for signal processing of structural systems
Jeng-Wen Lin and Tzung-Han Wu

    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 large-dimensional 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 Levenberg-Marquardt 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
Jeng-Wen Lin and Tzung-Han Wu: Department of Civil Engineering, Feng Chia University, Taichung 407, Taiwan, R.O.C.

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