A 'Model-on-Demand' identification methodology for non-linear process systems
A 'Model-on-Demand' identification methodology for non-linear process systems
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DOI：
10.1080/00207170110089734
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发表时间：
2001-12-01
影响因子：
2.1
通讯作者：
Stenman, A
中科院分区：
文献类型：
--
作者：
Braun, MW;Rivera, DE;Stenman, A
提出了一种基于多级伪随机序列（多级PRS）输入信号和“按需模型”（MOD）估计的单输入单输出非线性过程应用的辨识方法。“按需模型”估计允许对非线性系统进行准确预测，同时需要很少的用户选择，并且不需要解决非凸优化问题，这通常是全局建模技术的情况。通过允许用户将先验信息纳入多级PRS输入信号的设计变量的规范中，以“工厂友好”的方式生成用于MoD估计的足够信息的输入-输出数据集。该方法的实用性证明在案例研究中涉及识别的模拟快速热处理（RTP）反应器和中试规模的盐水混合罐。在由此产生的数据集上，MOD估计显示出与通过半物理建模和半物理建模与神经网络相结合所实现的性能相当的性能。然而，国防部估计器，实现了这一水平的性能，大大降低工程工作。
An identification methodology based on multi-level pseudo-random sequence (multi-level PRS) input signals and 'Model-on-Demand' (MoD) estimation is presented for single-input, single-output non-linear process applications. 'Model-on-Demand' estimation allows for accurate prediction of non-linear systems while requiring few user choices and without solving a non-convex optimization problem, as is usually the case with global modelling techniques. By allowing the user to incorporate a priori information into the specification of design variables for multi-level PRS input signals, a sufficiently informative input-output dataset for MoD estimation is generated in a 'plant-friendly' manner. The usefulness of the methodology is demonstrated in case studies involving the identification of a simulated rapid thermal processing (RTP) reactor and a pilot-scale brine-water mixing tank. On the resulting datasets, MoD estimation displays performance comparable to that achieved via semi-physical modelling and semi-physical modelling combined with neural networks. The MoD estimator, however, achieves this level of performance with substantially lower engineering effort.