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Updating structural parameters: an adaptive neural network approach

机译:更新结构参数:自适应神经网络方法

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An adaptive neural network (NN) method is proposed for the mode l updating and the damage detection of structures. The NN model is first trained off-line and is retrained during iteration if needed. An improved back-propagation learning algorithm with a jump factor and a dynamical learning rate is developed to facilitate the training. The concept of orthogonal array is adopted in this study to reduce the number of training samples required. Two examples illustrate that the proposed technique is quite useful for the model updating and the damage detection of structures.
机译:提出了一种自适应神经网络(NN)方法,用于模式L更新和结构的损伤检测。 NN模型首先触及培训,如果需要,在迭代期间在迭代期间再培训。开发了一种具有跳跃因子和动态学习率的改进的反向传播学习算法,以便于培训。本研究采用正交阵列的概念,以减少所需的训练样本的数量。两个示例说明了所提出的技术对于模型更新和结构的损坏检测非常有用。

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