中文摘要：

复杂化工过程建模对于工艺操作变量优化、指导技术决策具有重要意义，人工神经网络是其广泛采用的建模工具．但化工过程往往是复杂非线性动态系统，而描述其过程的神经网络模型往往是一个静态映射．没有考虑也很难考虑其操作变量与内部状态变量共同对目标性能的影响，从而导致依赖静态模型的技术决策效果不稳定．将静态过程模型看成是复杂非线性动态模型在操作变量子空间上的投影模型，为保证该投影模型实时逼近理想的非线性动态模型的精度，提出用Kalman滤波实时更新神经网络模型的权值，建立基于Kalman滤波神经网络子空间逼近的非线性动态工艺演化建模方法．鉴于扩展Kalman滤波的计算复杂性和精确性，采用无迹卡尔曼滤波刷新神经网络模型的权值．最后，把该方法应用于氢氰酸（HCN）工艺过程的动态演化建模试验，结果表明，该方法高精度地跟踪了非线性动态演化化工过程．因此，基于Kalman滤波神经网络子空间逼近的建模方法适用于非线性动态工艺演化建模．

英文摘要：

The modeling of complex chemical process is of great significance for determining the optimal parameters. Artificial neural networks （ANNs）have proved themselves to be very useful in various modeling applications, because they can represent complex mapping functions. However, the ANNs model normally represent a static relation, can＇t describe the dynamic properties of the evolutional chemical process. This study the static ANNs model was regarded as the approximating model of the chemical process respect to the operational parameters in subspace. To make the static model can accurately describe the dynamic properties in real time, the Unscented Kalman Filtering（UKF） algorithm instead of the Extended Kalman Filter（EKF） algorithm was used to update ANNs weights for dynamic chemical process modeling, because the UKF performance superior to that of the EKF in computational complexity and precision. The proposed method was applied to approximate the nonlinear dynamic Hydrocyanic acid （HCN） process, numerical simulations showed that the proposed method was good at modeling the HCN process in high-precision. Therefore, the proposed method provided a new solution to getting the evolutional model of the complex nonlinear dynamic process.