中文摘要：

结合经验公式及实验比较对神经网络的结构进行优化，利用Levenberg—Marquardt算法改进传统误差反向传播算法。基于两个改进人工神经网络的组合运用，针对结构动力响应，建立了一种可预测有效控制力的神经网络预测控制策略。用一个网络辨识结构的动力响应，另一个网络预测有效控制力。数值仿真显示：该算法具有比传统BP网络的最速下降法高一阶的收敛速度；除个别时间步外，该文控制策略可较准确识别结构的动力响应、给出有效控制力。

英文摘要：

The constitution of neural networks was optimized by empirical equations and experimental comparison, and Levenberg-Marquardt （LM） Algorithm was used to improve the traditional Back-Propagation （BP） Algorithm. Based on the combined application of two neural networks, an improved neural network predictive control strategy was introduced to predict the effective control forces for structural dynamic responses. One network was used to identify structural dynamic responses and the other to predict the effective control forces The numerical simulation shows that Levenberg-Marquardt algorithm has one order higher rate of convergence than the steepest descent method based on the traditional BP network, and that the introduced control strategy succeeds in identifying structural dynamic responses and predicting effective control forces except for several time steps.