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辨识算法推导的困难性，线性系统的系统辨识技术是新的被充分理解的非线性系统。 这些困难在建立基于经验的模型时被切断，因为在现实世界的应用中不可避免的噪声。 本计画将使用回授类神经网路来进行非线性系统辨识。 定义网络的参数通过输入输出数据训练确定。 培训方法基本上是以梯度为基础的。 出于两个原因，还将对稳定性方法进行检查。 它提供了一个定性的衡量识别性能，并提供了一些见解，t提高梯度方法的效率。 这些方法本身并不是唯一的，直到现实世界的应用标准（较低的错误率，免疫噪声等）。都被考虑在内。 为了说明来自经验数据的噪声效应，稳健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所提出的问题的定性理解。 将进行数值模拟，以测试拟议的识别程序。 理论方法和经验方法的目标是相同的：揭示神经网络用于解决真实的世界问题的未探索方面。 所提出的研究的启发，并将通过工业规模的过程识别问题进行测试。 特别地，实验将在与甲醇-异丙醇蒸馏塔接口的Honeywell TDC-3000真实的时间工厂信息和控制系统上进行。 ***

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. ***