Research Initiation Award: Recurrent Neural Networks as Representation of Nonlinear Dynamical Systems

在系统控制中，过程的数学描述通常是控制器设计的先决条件。由于非线性系统固有的复杂性和识别算法的推导困难，线性系统的系统识别技术是一种新的很好理解的非线性系统。由于在实际应用中不可避免的噪声，这些困难在建立基于经验的模型时被切断。这个项目将使用递归神经网络来执行非线性系统辨识。通过输入输出数据训练确定定义网络的参数。训练的方法基本上是基于梯度的。由于两个原因，稳定性方法也将被检查。它提供了识别性能的定性度量，并为提高梯度方法的效率提供了一些见解。在考虑到实际应用标准（较低的错误率、不受噪声影响等）之前，这些方法本身并不是唯一的。为了考虑经验数据的噪声影响，鲁棒范数将被视为参数预测误差的度量。基于自适应系统理论和统计分析的理论分析将被用来获得对所提出问题的定性理解。将进行数值模拟以测试拟议的识别程序。理论和经验方法的目标是相同的：揭示神经网络未被探索的方面，以解决现实世界的问题。提出的研究受到启发，并将通过工业规模的工艺识别问题进行验证。特别是，实验将在霍尼韦尔TDC-3000实时工厂信息和控制系统上进行，该系统与甲醇-异丙醇精馏塔相连。＊＊＊

9309057 Si In system control, a mathematical description of a process is often a prerequisite to the controller design. The system identification techniques for linear systems are new well understood nonlinear systems due to the inherent complexity of nonlinear systems and the difficulty of deriving identification algorithms. These difficulties are severed in building empirically based models because of unavoidable noise in real-world applications. This project will use recurrent neural networks to perform nonlinear system identification. The parameters defining the network are determined through input-output data training. The methodology for training is essentially gradient- based. Stability methods will also be examined for two reasons. It provides a qualitative measure of identification performance and provides some insight as t improve the efficiency of gradient methods. These approaches themselves are not unique until real-world application criteria (lower error rates, immune to noise etc.) are taken into consideration. To account for noise effect from empirical data, robust norm will be considered as a measure of parameter prediction error. Theoretical analysis based on adaptive systems theory and statistical analysis will be employed to gain qualitative understanding of the proposed problem. Numerical simulation will be carried out to test proposed identification procedures. The goal of both theoretical and empirical approaches are the same: reveal unexplored aspects of neural networks for real world problems. The proposed research is inspired and will be tested by an industrial scale process identification problem. In particular the experiments will be conducted at a Honeywell TDC-3000 real time plant information and control system interfaced to a methanol- isopropanil distillation column. ***