The objective of System Identification is to estimate models that can reproduce the static and dynamic properties of the process being modeled. Some system identification methods require a high order linear model of the process. These models are adopted as the process most reliable description. In some applications, it is necessary to reduce the order of the high order model. An important question is what is the best order of the high order model? This work presents an optimization strategy that maximizes the fit index in the search for the best high order of SISO and MIMO ARX models. The search for the best time delays related to the high order model is also addressed. The results of the optimization strategy are compared to those obtained by the exhaustive search in two scenarios: comparison of the fit indices of the high order models and comparison of these indices in models with reduced order obtained from the high order models.

系统识别的目的是估计可以重现正在建模的过程的静态和动态属性的模型。一些系统识别方法需要过程的高阶线性模型。这些模型被作为过程中最可靠的描述。在某些应用中，有必要降低高阶模型的阶数。一个重要的问题是高阶模型的最佳阶是什么？这项工作提出了一种优化策略，可以在寻找最佳高阶SISO和MIMO ARX模型的过程中最大化拟合指数。还讨论了与高阶模型有关的最佳时间延迟的搜索。在两种情况下，将优化策略的结果与通过穷举搜索获得的结果进行比较：高阶模型的拟合指数，以及从高阶模型获得的降阶模型中这些指数的比较。